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52 results about "Sparse representation classifier" patented technology
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Robust speech emotion recognition method based on compressive sensing
InactiveCN103021406AImprove noise immunitySpeech recognitionMel-frequency cepstrumSparse representation classifier
The invention discloses a robust speech emotion recognition method based on compressive sensing. The recognition method includes generating a noisy emotion speech sample, establishing an acoustic feature extraction module, constructing a sparse representation classifier model, and outputting a speech emotion recognition result. The robust speech emotion recognition method has the advantages that effects of noise on emotion speech in the natural environment are fully considered, and the robust speech emotion recognition method under the noise background is provided; validity of feature parameters in different types is fully considered, extraction of feature parameters is extended to the Mel frequency cepstrum coefficient (MFCC) from prosodic and tone features, and anti-noise effects of the feature parameters are further improved; and the high-performance robust speech emotion recognition method based on the compressive sensing theory is provided through the sparse representation distinguishing in the compressive sensing theory.
Owner:TAIZHOU UNIV +2
CFAR (Constant False Alarm Rate) and sparse representation-based high-resolution SAR (Synthetic Aperture Radar) image ship detection method
ActiveCN103400156AEasy constructionReduce data volumeCharacter and pattern recognitionSynthetic aperture radarSparse representation classifier
The invention discloses a CFAR (Constant False Alarm Rate) and sparse representation-based high-resolution SAR (Synthetic Aperture Radar) image ship detection method, which mainly solves the problems of large data quantity to be processed and low real-time property existing in the conventional method. The method comprises the following detection steps: selecting a ship target training sample in a high-resolution SAR image and determining the size of a CFAR sliding window by the training sample; down-sampling the high-resolution image, performing image segmentation and land elimination on the high-resolution image, detecting in a low-resolution image by using the CFAR method and performing preliminary identification, and mapping a detected pixel point to a potential target region in the original high-resolution image; outputting potential target region slices obtained by all detection; and finally, extracting characteristic vectors of the potential target region slices respectively and identifying through a sparse representation classifier to obtain a final ship detection result. The CFAR and sparse representation-based high-resolution SAR image ship detection method has the advantages of high detection speed, high detection rate and low false alarm rate, and can be used for fishery supervision, maritime safety management and the like.
Owner:XIDIAN UNIV
Super-pixel polarimetric SAR land feature classification method based on sparse representation
InactiveCN104123555AMaintain spatial similarityImprove classification accuracyCharacter and pattern recognitionSparse representation classifierClassification methods
The invention discloses a super-pixel polarimetric SAR land feature classification method based on sparse representation. The method comprises: inputting polarimetric SAR image data to be classified, processing the image, and thereby obtaining a pseudocolor image corresponding to Pauli decomposition; performing super-pixel image over-segmentation on the pseudocolor image to obtain a plurality of super-pixels; extracting features, which are seven-dimensional, of radiation mechanism of the original polarimetric SAR image as features of every pixel; performing super-pixel united sparse representation to obtain sparse representation of each super-pixel feature; classifying by using a sparse representation classifier; working out the mean value of each super-pixel covariance matrix, then performing super-pixel complex Wishart iteration by using the classifying result in the last step, and at last obtaining a final classifying result. According to the super-pixel polarimetric SAR land feature classification method based on sparse representation, the problem that traditional classifying areas based on the single pixel are poor in consistency is solved, and operating speed of the algorithm is greatly increased on basis of improving accelerate.
Owner:XIDIAN UNIV
Hyperspectral image classification method based on multi-task low rank
ActiveCN104281855AThe classification method is reasonableEfficient use ofCharacter and pattern recognitionFeature vectorSparse representation classifier
The invention discloses a hyperspectral image classification method based on a multi-task low rank. The method mainly solves the problems that an existing method only uses spectral characteristics in the hyperspectral image classification process, hyperspectral characteristics cannot be described from multiple angles, and therefore the classification accuracy is low. The method includes the steps that firstly, a hyperspectral image is input; secondly, spectrum gradient characteristics of the hyperspectral image are extracted; thirdly, the spectral characteristics and the spectrum gradient characteristics serve as input signals and dictionaries of a multi-task low rank model, the model is resolved, and then two coefficient matrixes are acquired; fourthly, the two coefficient matrixes are connected according to lines, and a new coefficient matrix is acquired and serves as a new characteristic vector matrix of samples; fifthly, one part of the samples are selected as training sets, and the other part of the samples serve as test sets; sixthly, the training sets and the test sets are input in a sparse representation classifier, and then a classification result is acquired. Compared with a traditional low-rank model classification method, cross characteristic information is effectively utilized, and compared with an exiting image classification method, the high classification accuracy is acquired.
Owner:XIDIAN UNIV
Human face emotion identifying method based on Bayes fusion sparse representation classifier
ActiveCN105160299ACope wellImprove accuracyAcquiring/recognising facial featuresNoseSparse representation classifier
The invention discloses a human face emotion identifying method. The method includes pretreatment, image division, feature extraction, classification and classification result fusion of human face expression pictures. The method is characterized in that a complete human face expression picture is divided into four sub images (corresponding to the forehead, the eyes, the nose and the mouth) according to the distribution of the five sense organs; a sparse representation classifier is utilized for classifying the sub pictures and the original picture so as to obtain five possible classification results; finally the weighing Bayes fusion decision theory is utilized for adjusting the weight distribution of the five sense organs and the similarity and difference between expressions are both taken into consideration. The method has advantages of being simple to implement, good in noise prevention robustness and being capable of handling complicated human face expression identification well and improving human face expression identification accuracy and the like.
Owner:SOUTH CHINA UNIV OF TECH
Hyperspectral image classification and wave band selection method based on multi-target immune cloning
InactiveCN103914705AReduce the numberEnsure regional consistencyCharacter and pattern recognitionMean-shiftSparse representation classifier
The invention discloses a hyperspectral image classification and wave band selection method based on multi-target immune cloning. The hyperspectral image classification and wave band selection method based on multi-target immune cloning comprises the following steps that a sparse representation classifier is used for classifying hyperspectral remote sensing images so as to obtain classified images based on pixels; the hyperspectral remote sensing images are preprocessed by means of mean shift, the processed images are segmented into a plurality of clustering results through a multi-target immune cloning clustering and wave band selection algorithm, and the optimal clustering results are selected from the clustering results so as to constitute a segmentation image; the obtained classified images and the obtained segmentation image are fused by means of the maximum voting rule so as to obtain a final result image. The hyperspectral image classification and wave band selection method based on multi-target immune cloning has the advantages that the very high accuracy rate and Kappa coefficient values can be obtained under the circumstance that few training samples exist, region consistency is well kept, the hyperspectral image classification and wave band selection method is suitable for multiple hyperspectral data, and parameters are adjusted easily and conveniently.
Owner:XIDIAN UNIV
Surface normal component graph neural network representation-based three-dimensional face recognition method
PendingCN107844760AExcellent feature learningImprove recognition accuracyCharacter and pattern recognitionSparse representation classifierGraph neural networks
The invention relates to a surface normal component graph neural network representation-based three-dimensional face recognition method. The method includes the following steps that: a given three-dimensional face surface is preprocessed; the three-dimensional coordinates of the pre-processed three-dimensional face surface are projected onto a two-dimensional plane, projected coordinate information is utilized to estimate the normal vectors of each point, so that normal component graphs in an X, Y, and Z direction are generated; a deep convolutional neural network trained in 2D face recognition is utilized to extract the features of each normal component graph of the three-dimensional face; and the depth features of each normal component graph are extracted under a nerve cell position-sensitive matching mode, and a nearest neighbor classifier or a sparse representation classifier is used to achieve the comparison of the three-dimensional face. The three-dimensional face recognition technology of the present invention has the advantages of simplicity, easiness in implementation, high robustness to expressions, high recognition accuracy and the like.
Owner:XI AN JIAOTONG UNIV
Robust face recognition method based on dictionary decomposition and sparse representation
ActiveCN106326871ASolve the low recognition rateImprove recognition rateCharacter and pattern recognitionData setDecomposition
The invention belongs to the field of pattern recognition, and particularly relates to a robust face recognition method based on dictionary decomposition and sparse representation. The method comprises the steps of designing a dictionary decomposition model to extract class specific information in a face image from a given face image training data set, then calculating a mapping matrix to describe a mapping relation between the class specific information and original training data, correcting the tested image according to the calculated mapping matrix, then reducing the dimensionality by using principal component analysis (PCA), and finally performing recognition classification via a sparse representation classifier (SRC). The method can effectively avoid the problem that the recognition rate is greatly reduced in the SRC recognition process because the training data is polluted or shaded or missing, and can achieve a high and stable recognition effect.
Owner:CHINA JILIANG UNIV
SAR image search method based on sparse coding classification
ActiveCN103714148AImplement classificationReduce computationSpecial data processing applicationsMemetic algorithmPattern recognition
The invention provides an SAR image search method based on sparse coding classification. The SAR image search method aims at the defects of an existing image search system and method. Through extraction of characteristics and construction of an over-complete dictionary, solution is carried out through sparse representation based on a dual Memetic algorithm, a sparse representation classifier is trained, the classification process with supervision can be achieved in the classification process, the sparse solution with global optimum can be solved fast, and then search results are output from high to low according to similarity. When the problem of image classification is solved, the method achieves the good improvement effect on classification accuracy, search content similarity, calculating complexity and result robustness.
Owner:XIDIAN UNIV
Design method for linear discrimination of sparse representation classifier based on nuclear space
ActiveCN105868796APreserve local geometric featuresImprove recognizabilityCharacter and pattern recognitionTest sampleAlgorithm
The invention relates to a design method for linear discrimination of a sparse representation classifier based on nuclear space. The method comprises the following steps of reading training samples, performing nonlinear transformation on the training samples to transform the training samples to the high-dimensional nuclear space, learning the training samples of each kind in the high-dimensional nuclear space, finding out the contribution (namely the weight) made by each individual in the training samples of the kind to constructing the subspace of the training sample of the kind, forming dictionaries through products of the training samples of the kind and a weight matrix, and sequentially arranging the dictionaries of all kinds to form a large dictionary matrix; obtaining linear discrimination sparse codes of the test samples inside the nuclear space on the basis of the dictionary matrix, and performing fitting on the test samples through the dictionaries of each kind and linear discrimination coding corresponding to the dictionaries; adopting the kind with the minimum fitting error as the category of the test samples. It can be ensured that sparse codes of the samples of the same kind are concentrated, sparse codes of the samples of different kinds are dispersed, the sample discrimination is effectively improved, and the performance of the classifier is improved.
Owner:CHINA UNIV OF PETROLEUM (EAST CHINA)
Image classification method based on combination of SRC and MFA
ActiveCN104794498AAvoid the problem of inaccurate classification resultsImprove classification recognition rateCharacter and pattern recognitionHat matrixTest sample
The invention discloses an image classification method based on combination of SRC and MFA, which is mainly used for solving the problem that the image classification result is not ideal because only the reconstruction relationship or local discriminant structure is considered and the sample information cannot be accurately described in the conventional feature extraction method. The method comprises the following steps: 1. inputting a training sample and a test sample, constructing the same kind and different kinds of sample matrixes, and initializing a projection matrix; 2. projecting the training sample, respectively taking the same kind and different kinds of samples as dictionaries, solving sparse representation coefficients of the samples, and constructing the same kind and different kinds of sparse weight matrixes; 3. constructing an objective function to solve a novel projection matrix; 4. iterating the steps 2 and 3 until the cycle index is larger than the set initial value, outputting the final projection matrix, and projecting the test samples; and 5. classifying the test samples by utilizing a sparse representation classifier. According to the method disclosed by the invention, the accuracy of image classification is enhanced, and the method can be used for discriminating identity of characters or searching objects during image shooting in a police work system.
Owner:XIDIAN UNIV
Facial emotion recognition method based on local sparse representation classifier
InactiveCN104636711AImprove accuracyEmotion recognition is fastAcquiring/recognising facial featuresFeature vectorSparse representation classifier
The invention discloses a facial emotion recognition method based on a local sparse representation classifier. The method is characterized by including: collecting facial emotion images; using Gabor wavelet transformation to construct the feature vectors of the facial emotion images; using a feature selection algorithm MFCS to select features; using the local sparse representation classifier to recognize emotion classification. Compared with the prior art, the method is high in emotion classification recognition accuracy, fast in emotion recognition, insensitive to faces, and the like.
Owner:GUANGZHOU HUAJIU INFORMATION TECH
Facial expression recognition method
ActiveCN107563312AEfficient extractionImprove computing efficiencyCharacter and pattern recognitionHat matrixDimensionality reduction
The invention discloses a facial expression recognition method. According to the method, texture feature extraction is carried out on a facial expression image by using a multi-scale-parameter MB-LBPoperator and unified-mode pixel histogram statistics is carried out; HOG feature extraction is carried out on facial image samples; series-connection feature fusion is carried out on MB-LBP features and the HOG features in a same-kind mode; in fused feature space, a training sample is extracted randomly and the rest of samples are used as testing sample; PCA dimensionality reduction calculation iscarried out on the training sample to obtain a projection matrix W, the training sample is projected to low-dimensional sub space to obtain a feature expression of an expression image in the low-dimensional sub space; and the testing samples are projected to the low-dimensional sub space by the projection matrix W and the features of the testing samples are classified by using a sparse representation classifier to obtain the types of the testing samples. Therefore, an expression feature with texture and shape information is expressed by using a low dimension number, so that a high expressionrecognition rate is obtained; and the recognition accuracy is high.
Owner:NANCHANG HANGKONG UNIVERSITY
Pest image classification method based on context sensing dictionary learning
ActiveCN104102922AHigh precisionImprove efficiencyCharacter and pattern recognitionDictionary learningContext sensing
The invention provides a pest image classification method based on context sensing dictionary learning. The method comprises the following steps that: context sensing information of pest images in the known category is added into a pest image sample base to obtain a plurality of types of training samples, a learning function is constructed, and the training samples are used for completing pest image redundant dictionary learning; the pest images to be classified are subjected to preprocessing to obtain test samples; the test samples are subjected to sparse representation dimensionality reduction processing; the test samples subjected to the sparse representation dimensionality reduction processing are read into a sparse representation classifier, and the residual error of the context sensing information of the test samples and various types of the training samples is calculated according to a redundant dictionary obtained through learning; and the residual error of the context sensing information of the test samples and various types of the training samples is analyzed, and the categories of the test samples are determined. The pest image classification method has the advantages that the precision and the efficiency of the pest image classification in complicated scenes can be improved, and a traditional crop pest diagnosis mode is improved.
Owner:HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI +1
Distributed compressed sensing data classification method based on sparse representation classifier
ActiveCN102932847AReduce energy consumptionEasy to manageEnergy efficient ICTPower managementClassification methodsSparse representation classifier
The invention requests protection of a distributed compressed sensing data classification method based on a sparse representation classifier, relating to the field of wireless networks. Specific to that the characteristics of overload in data transmission and processing of sending nodes and data sparseness relevancy are not fully considered when the traditional classification method is applied to a sensor network with larger scale or more applications, a classification method suitable for relevant sparse data is designed, wherein a common part and a special part of data sparse coefficients are taken as classification basis so as to process sensor data reasonably. The data classification method provided by the invention is more accurate in data classification result of the sensor network and meanwhile has the advantages of effectively reducing network energy consumption by using a sparse coefficient strategy, improving the algorithm efficiency and the data processing performance, and facilitating user management as well as network application and scale expansion.
Owner:CHONGQING UNIV OF POSTS & TELECOMM
Pedestrian tracking method of single camera
ActiveCN106682573ASolve the problem of track interruptionDealing with the same target appearance changesBiometric pattern recognitionSparse representation classifierTwo step
The invention discloses a pedestrian tracking method of a single camera. The method adopts a hierarchical tracking strategy. Firstly, detection targets are linked into stable and reliable short segments of movement tracks, and then a final tracking track is formed by matching the short segments of the movement tracks and filling blank segments between the short segments of the movement tracks. The hierarchical tracking strategy helps solve the problem of track interruption caused by short time of blocking of pedestrians in the tracking process. In the constructions of the short segments of the movement tracks, the method proposes a two-step matching method based on a cascading idea. Firstly, a high-efficient histogram matching method is used to deal with a condition of small matching difficulty, and then a robust sparse representation classifier is adopted to solve the aliasing problem caused by target apparent similarity. In the matching of the short segments of the movement tracks, a method of trajectory distance measurement based on sparse representation is proposed, which has the preferable robustness to the apparent change of target caused by environment change and target deformation. Furthermore, a track matching method, with small computation, based on hierarchical clustering, is also proposed, thereby improving the efficiency of the global multi track matching.
Owner:SUN YAT SEN UNIV
BIT (built-in test) intermittent-fault diagnosis method based on sparse representation
PendingCN108171277AReduce false alarm rateEffective diagnosisCharacter and pattern recognitionError identificationPattern recognitionDiagnosis methods
The invention discloses a BIT (built-in test) intermittent-fault diagnosis method based on sparse representation. The method specifically includes the following steps: (1) collecting different equipment history BIT data of which types include a normal data type, an intermittent-fault data type and a permanent-fault data type, selecting 70% of data from each data type to use the same as a trainingsample set, and removing labels of the remaining data to use the same as a test sample set; (2) utilizing a k-singular-value-decomposition (K-SVD) learning algorithm to carry out learning training onthe training sample set, and constructing an overcomplete dictionary D; (3) utilizing the overcomplete dictionary D to carry out sparse representation on the obtained test sample set to obtain sparsecoefficients; (4) utilizing a sparse representation classifier (SRC) to carry out sparse reconstruction on the sparse coefficients to obtain reconstruction residuals; and (5) categorizing to-be-testedsamples into categories of smallest reconstruction residuals, and thus obtaining diagnosis results of to-be-tested BIT data. The method can highly effectively and accurately diagnose BIT intermittent-faults, has higher diagnosis precision, and decreases a BIT false-alarm rate.
Owner:CHONGQING UNIV
Astragalus membranaceus spectrogram feature extraction and production place identification method based on kernel principal component analysis
InactiveCN110097127AGreat applicationMaterial analysis by electric/magnetic meansCharacter and pattern recognitionPrincipal component analysisSparse representation classifier
The invention discloses a spectrogram feature extraction algorithm based on kernel principal component analysis, and on the basis, a sparse representation classifier is applied to analyze an astragalus membranaceus spectrogram, so that a method for quickly identifying the production place of astragalus membranaceus is realized. The chemical analysis method adopted by the invention mainly comprisesan ion mobility spectrometry technology and an ultraviolet fluorescence spectrum analysis technology, has the advantages of rapid spectrum collection, good discrimination and the like, and is an important method for rapidly identifying the quality of the Chinese herbal medicine. According to the technical scheme, the method mainly comprises the steps of collection of astragalus membranaceus spectrogram information, extraction of Chinese herbal medicine spectrogram characteristics based on kernel principal component analysis and identification of the astragalus membranaceus production place based on a sparse representation classifier. Experiments show that the method is high in operation speed and high in recognition rate, provides a technical solution idea and scheme for rapid quality identification of Chinese herbal medicines, and has wide application significance.
Owner:杭州麦迪特检测技术服务有限公司 +2
Cell classifying method based on EMD feature extraction and sparse representation
ActiveCN106096571AQuick discoveryImprove accuracyImage enhancementImage analysisData decompositionSparse representation classifier
The invention discloses a cell classifying method based on EMD feature extraction and sparse representation, and relates to an EMD-based cell feature extraction method. The method comprises steps: an orthogonal subspace projection (OSP) method is firstly used for carrying out band section on a medical hyperspectral image, dimensions are reduced, and data redundancy is reduced; and then, a two-dimensional EMD method is used for carrying out feature extraction on the data after dimension reduction, and the data are decomposed into a series of IMF components whose frequencies are arranged from high to low. A sparse representation classifier (SRC) is used for classifying the data, samples are classified through comparing residual, and when the residual obtained through calculation is small, the sample is divided into the class. In the cell classifying method based on EMD feature extraction and sparse representation, the EMD shows good time frequency characteristics, and an obvious potential and advantages exist in hyperspectral data feature extraction. Meanwhile, as the sparse representation classifier (SRC) is used, the classifying precision is more greatly ensured.
Owner:BEIJING UNIV OF CHEM TECH
Sparse-representation-classification-based pavement crack detection and identification method
The invention provides a sparse-representation-classification-based pavement crack detection and identification method. A sparse representation-based classifier (SRC) is introduced and an effective sub-block high-order moment characteristic is selected, thereby avoiding pretreatment and post treatment of an image, simplifying detection steps, and improving the operation efficiency. The method comprises: carrying out extraction and normalization of training set (subblock) characteristic vectors; dividing a tested image into a plurality of sub blocks and extracting features of the sub blocks, and carrying out classification by using an SRC; and identifying a crack type according to a mapping code of a sub block classification result. Compared with the traditional crack detection and identification method, the provided method has the higher identification precision and higher execution efficiency.
Owner:NANJING UNIV OF SCI & TECH
Cooperative sparse representation self-adaptive rapid face recognition method
ActiveCN105825205ADecoupling dependenciesIncrease calculation rateCharacter and pattern recognitionTest sampleSparse representation classifier
The invention relates to a cooperative sparse representation self-adaptive rapid face recognition method. The method includes a local sparse representation classifier system that does not violate a sparse representation definition fundamental assumption, and includes the steps of: reading in images of training samples and a test sample; initializing the training samples and the test sample, using bilinearity interpolation to scale the training samples and the test sample to images of fixed sizes, integrating into column vectors and performing normalization processing; using nucleus induction to find out N* training samples most adjacent to the test sample, N* being an optimal predicted value; picking out a training sample category related with the test sample from the N* training samples to form a complete base; and using I<2> norm collaboration to solve a sparse coefficient and predicting the category of the test sample through a residual error. The method also includes a system capable of finding the optimal predicted value N* according to different training sample libraries. The rapid face recognition method provided by the invention solves the problem of balancing the recognition rate and the calculation speed, and enables a whole recognition system to automatically search an appropriate N value for different training libraries.
Owner:FUZHOU UNIV
A facial expression recognition method based on MB-2DPCA features
InactiveCN107506718AHigh facial expression recognition accuracySolve the small sample problemAcquiring/recognising facial featuresHat matrixSmall sample
The invention discloses a facial expression recognition method based on MB-2DPCA features. The method comprises the following steps: carrying out local texture feature extraction on a facial expression image through a multi-scale parameter MB-LBP operator; carrying out feature extraction on expression features and constructing an MB-LBP feature space of the image; calculating a projection matrix W through a 2DPCA algorithm in training samples, and mapping all of the training samples to a subspace through the projection matrix W, and obtaining feature expression of the expression image in the subspace; and mapping a test sample to the subspace through the projection matrix W, and carrying out classification on the features of the test sample through a sparse representation classifier to obtain the category of the test sample. The method prevents the problem of large covariance matrix calculation dimensions due to conversion from an image matrix to the image vector, and the problem of small samples due to fewer samples; and the calculation time is less.
Owner:NANCHANG HANGKONG UNIVERSITY
3D palmprint identification method
ActiveCN108960125AReduce usageEasy to identifyMatching and classificationThree-dimensional object recognitionImage alignmentSparse representation classifier
The invention discloses a 3D palmprint identification method. In the method, only the local features of blocks of a 3D palmprint, namely, a phase and surface type block histogram, are adopted. A 3D palmprint feature model is proposed. The identification effect is improved, and moreover, the use of a 2D palmprint image is avoided so that the whole system is free from the influence of light intensity and scratch marks. The local features of the 3D palmprint adopted are robust to small translation, rotation and even zooming of images, so that there is no need to use multiple translation, nearestiteration, cross-correlation and other methods for image alignment, and the identification efficiency is improved. An intermediate term is added in a sparse representation classifier, and an improvedsparse representation classifier is proposed by improving the sparse coefficient. The sparse coefficient can be calculated before 3D palmprint classification, and a subspace method is used to compareimages in the classification process. Thus, when there are a lot of samples in the training library, there is no need to compare testing samples and training samples one by one. The data redundancy, computation and processing time are reduced.
Owner:HEBEI UNIV OF TECH
Design method of elastic network constraint self-interpretation sparse representation classifier
PendingCN106250929AImprove classification accuracyReduce fit errorCharacter and pattern recognitionElastic networkAlgorithm
The invention relates to a design method of an elastic network constraint self-interpretation sparse representation classifier. The method comprises the following steps: training samples are read, the training samples are linearly transformed to a high-dimensional kernel space, each type of the training samples are learnt in the high-dimensional space, a contribution (i.e., a weight) made by each individual in the type of the training sample to constructing a sub-space of the type of the training samples is found, and a dictionary is constructed by a product of the type of the training samples and a weight matrix; and elastic network coefficient coding of test samples in the kernel space is obtained through training the obtained sparse representation dictionaries, and finally, the test samples are fitted by use of each type of the dictionaries and the elastic network sparse coding corresponding to the dictionaries, fitting errors are calculated, and the type of minimum fitting errors are the type of the test samples. According to the invention, the method is integrated with the advantages of ridge regression and lasso regression, sparse coding features of the samples are enabled to sparse, the fitting errors are also quite small, classification errors are effectively reduced, and the identification performance of a classifier is improved.
Owner:CHINA UNIV OF PETROLEUM (EAST CHINA)
Plant identification method based on elliptical Fourier descriptors and weighted sparse representation
InactiveCN107392225AImprove robustnessImprove recognition rateImage analysisCharacter and pattern recognitionData setSparse representation classifier
The invention provides a plant identification method based on elliptical Fourier descriptors and weighted sparse representation. The plant identification method is mainly and technically characterized by comprising the steps that leaf images are preprocessed, wherein all the colored leaf images are converted into grayscale images, the leaf images are separated from the background through an Otsu segmentation algorithm and converted into binary images, and small holes of the leaf images are eliminated through an erosion algorithm; edge detection is conducted by adopting a Canny edge detector; centroids of boundaries are calculated; the Fourier descriptors are calculated; a complete dictionary is constructured, wherein Fourier descriptor vectors of all leaf image data sets are divided into training sets and test sets, and the complete dictionary is composed of the Fourier descriptor vectors of all the training sets; and optimization is conducted by a weighted sparse representation classifier. According to the plant identification method, by adopting the elliptical Fourier descriptors, good robustness is achieved on noise and other factors, and by applying the weighted sparse representation classifier (WSRC) to plant species identification and particularly to a low-dimension space, the identification rate is obviously increased.
Owner:TIANJIN UNIV OF SCI & TECH
Sparse representation-based residual current waveform automatically identifying method
ActiveCN107578016AReal-time automatic recognitionAccurate identificationCharacter and pattern recognitionFeature vectorPower flow
The invention discloses a sparse representation-based residual current waveform automatically identifying method. The sparse representation-based residual current waveform automatically identifying method comprises collecting residual current signals of N different devices during leakage fault through a residual current device (RCD), performing de-noising treatment to acquire N preprocessed signals as a sample set; extracting time-frequency domain characters of signals inside the sample set to acquire character vectors, and performing normalization treatment; combining the normalized charactervectors into a character matrix of all the signals as an over-complete dictionary of the N residual current signals; collecting residual current signals during leakage fault through the RCD, performing de-noising treatment to obtain a sample to be tested, and extracting the character vectors of the sample to be tested; performing sparse representation on the extracted character vectors through the over-complete dictionary; inputting the sparse representation of every signal into a sparse representation classifier to acquire an identification result of the type of residual current signals to be tested. The sparse representation-based residual current waveform automatically identifying method can improve effectiveness and accuracy of residual current waveform identification.
Owner:CHONGQING UNIV
Speaker verification method and system based on tensor structure and sparse representation
ActiveCN110010137AImprove confirmation efficiencyRetain the inner structureSpeech analysisFeature vectorSpeaker verification
The invention discloses a speaker verification method and system based on a tensor structure and sparse representation. The method comprises the following steps of Step 1, building an auditory featuretensor; Step 2, converting the auditory feature tensor into a spare matrix; Step 3, reducing the dimension of the sparse matrix so as to generate a final feature vector; and Step 4, performing speaker verification on the basis of a sparse representation classifier. The speaker verification method and system have the advantages that on the basis of remaining an internal structure of data, the calculation complexity is reduced; and the speaker verification efficiency is improved.
Owner:HANGZHOU DIANZI UNIV
A Robust Face Recognition Method Based on Dictionary Decomposition and Sparse Representation
ActiveCN106326871BSolve the low recognition rateImprove recognition rateCharacter and pattern recognitionDecompositionSparse representation classifier
The invention belongs to the field of pattern recognition, in particular to a robust face recognition method based on dictionary decomposition and sparse representation. Design a dictionary decomposition model to extract the class-specific information in the face image from the given face image training data set, and then calculate a mapping matrix to describe the mapping relationship between the class-specific information and the original training data, and get The mapping matrix of the test image is corrected, and then the principal component analysis (PCA) is used to reduce the dimensionality, and finally the sparse representation classifier (SRC) is used for recognition and classification. The invention can effectively avoid the problem that the recognition rate is greatly reduced due to the contamination, occlusion, or lack of training data during the SRC recognition process, and can obtain a high and stable recognition effect.
Owner:CHINA JILIANG UNIV
Pavement Crack Detection and Recognition Method Based on Sparse Representation Classification
Owner:NANJING UNIV OF SCI & TECH
A Single Camera Pedestrian Tracking Method
ActiveCN106682573BImprove reliabilityImprove matching efficiencyBiometric pattern recognitionAlgorithmSparse representation classifier
Owner:SUN YAT SEN UNIV
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