51results about How to "Improve annotation accuracy" patented technology

The invention discloses a performance fragment marking method, a video playing method, a video playing device, and a video playing system, and belongs to the multimedia technology field. The performance fragment marking method comprises steps that a multimedia file corresponding to a role is acquired; according to the multimedia file, the characteristic of the role can be determined; a target video is decoded to acquire a data frame and a plating time stamp corresponding to the data frame; a target data frame matched with the characteristic of the role is identified in the data frame of the target video; according to the playing time stamp of the target data frame, performance fragment information corresponding to the role is marked automatically. According to the invention, a server can be used for the automatic marking of a plurality of target videos at the same time, and therefore problems of operation editors of low efficiency and poor accuracy caused by the fact that the operationeditors can only mark a small part of vides in a limited time period can be solved, and effect of marking the target video effectively in the limited time period can be achieved.

The invention discloses a movie and television play role labelling method based on search matching. The method comprises the following steps of: obtaining the to-be-labelled object set of a labelling scene and all to-be-labelled object information according to a to-be-labelled object list; constructing a text keyword for each of to-be-labelled objects, and obtaining the corresponding image set by virtue of an image search engine; carrying out face detection and visual attribute analysis on the image of the search result, and removing a noise therein to obtain a role face set which is closely related to the labelling scene, of the to-be-labelled objects; carrying out face detection and tracking on the labelling scene to obtain all face sequences therein; carrying out role labelling on the labelling scene on the basis of a visual similarity among the face sequences, and a visual similarity analysis on the face sequences and the role faces of the to-be-labelled objects. According to the method disclosed by the invention, movie and television play role labelling is carried out by virtue of face images related to movie and television play roles in the Internet; the method disclosed by the invention has the beneficial effects that the labelling process is fully-automatic, high in labelling accuracy, and high in method extensibility and universality.

The invention relates to a cross-domain knowledge transfer tag embedding method and apparatus. The method comprises the steps of obtaining text data of a source domain and a target domain, performing model representation, solving word vector parameters of keywords in the source domain and the target domain, and performing transfer of keyword tags from the source domain to the target domain; obtaining nearest neighbors of labeled keywords in the source domain and the target domain, performing weight assignment on keywords of the nearest neighbors by keyword tags of the labeled keywords to obtain extended keyword tags; performing user-level keyword tag labeling according to extracted user-level text data; dynamically optimizing parameters of user-level keyword tag parts according to click and / or access data information of a user based on the word vector parameters of the keywords and the user-level keyword tags; and obtaining new user-level text data from the target domain, performing user-level keyword tag labeling prediction and sorting, and outputting a result. According to the method and the apparatus, the accuracy and high efficiency of tag labeling can be taken into account and business demands of business personnel are met.

The invention relates to an image annotation method comprising the following steps: 1, defining a target function of an image annotation model; 2, inputting an image into a CNN model so as to obtain original image characteristics; 3, weighting the original image characteristics; 4, inputting information into a LSTM model; 5, making reversed propagations for errors caused by prediction result. Theimage annotation method firstly uses a convolution nerve network to extract image bottom layer characteristics, then uses a focusing mechanism to extract image characteristics related to image annotation words in an image specific position area, inputting the information into the LSTM network model, forming corresponding prediction annotation words, and finally realizing image annotations; the image annotation method is excellent in annotation performance, high in annotation precision, thus well satisfying real application demands.

The invention discloses a target detection method and device, and the method comprises the steps: obtaining an initial image, an initial boundary frame being marked for an interest target in the initial image; inputting the initial image into a pre-trained regression model to obtain a to-be-segmented image which is output by the regression model and comprises an adjusted boundary frame, the regression model being obtained by pre-training a training image marked with a real boundary frame of the interest target and marked with the initial boundary frame of the interest target; and segmenting the image contained in the adjusted boundary frame in the to-be-segmented image as a to-be-classified image containing the interest target. According to the method and the device, the marking precisionof the boundary frame of the interest target is improved.

The invention relates to a calibration method of a laser radar and a camera, and the method comprises the following steps: enabling the laser radar to obtain N frames of point cloud data comprising a calibration board, eliminating the random error of the laser radar, and finding out the edge points of the calibration board; setting a transformation matrix between the calibration plate and the radar coordinate system as a T1 pseudo camera coordinate system, transforming the edge points of the calibration plate to the pseudo camera coordinate system, and projecting the edge points to an imaging plane of a pseudo camera to obtain a projection image; finding a 2d coordinate of an angular point of the calibration plate in a pseudo camera coordinate system from the projection image; and obtaining 3d coordinates of the angular points of the calibration plate in the camera coordinate system, thereby obtaining a conversion matrix T2 between the camera coordinate system and the pseudo camera coordinate system, and further obtaining a conversion matrix T = T2-1 * T1 between the laser radar coordinate system and the camera coordinate system. According to the method, random errors caused by laser radar scanning can be eliminated, so that the marking precision is improved.

The invention discloses an automatic ink-jet printing system, which can ensure an automatic ink-jet printer to accurately complete automatic marking work, and comprises a positioning ink-jet printing platform, a longitudinal moving device, a transverse moving device, a camera and an ink-jet printing head, wherein the positioning ink-jet printing platform is fixed on a supporting beam between two wall plates of the whole system, the longitudinal moving device is slidably arranged at both sides of the positioning ink-jet printing platform, the transverse moving device is slidably arranged on the connection frame of the longitudinal moving device, the camera is arranged above two diagonal lines of the positioning ink-jet printing platform through a support, and the ink-jet printing head is fixed on the transverse moving device. The automatic ink-jet printing system can complete the automatic marking work about geographic information changes in a map or the automatic map printing work in cooperation with the automatic air draught positioning system, and has the advantages of being high in automation degree and marking accuracy, small in error, standard and regularized in marking work, good in consistency, and high in working efficiency.

A remote sensing multispectral data semi-supervised labeling method based on self-learning relates to the field of data labeling, and comprises the following steps: acquiring remote sensing multispectral image data in a research area, determining category information of a to-be-classified target, and performing image fusion on the remote sensing multispectral image data; selecting a sample plot in the research area, recording category information of a to-be-classified target in the sample plot, determining a pixel corresponding relation between the to-be-classified target in the sample plot and the fused remote sensing multispectral image data in combination with the fused remote sensing multispectral image data, and obtaining pixel category information of the fused remote sensing multispectral image data; taking the seed point data as initial labeled data, and removing redundant information between wavebands by using a principal component analysis method; using the processed data with the labels to construct a classification model by adopting a random forest algorithm; the method comprises the following steps: classifying unlabeled data, removing abnormal points, and obtaining a self-labeling data set after multiple iterations; the method has the advantages of low requirement on manual labeling data volume, high precision, fast classification speed and strong anti-noise capability.

The invention discloses an lncRNA identification and expression quantification analysis method, which is characterized by comprising a series of a sequencing data filtering step, a sequencing data comparison step, a transcription splicing step, a gene comparison step, an lncRNA filtering step and an lncRNA analysis step. According to the method, the new lncRNA can be discovered, and the annotationaccuracy of the lncRNA is improved.

The invention discloses a data labeling method and device, and the method comprises the steps: inputting each piece of data in a to-be-labeled data set into K labeling models, and obtaining K labels for each piece of data, wherein the K labeling models are obtained through the training of K sub-training sets, the K sub-training sets are obtained by performing K times of random sampling with replacement on samples in a total training set, and K is an integer greater than 1; dividing the data corresponding to the label into samples with different confusion degrees based on the confidence coefficient of the label, the confidence coefficient being the consistency degree of K labels obtained for each piece of data; and in a preset stage, sequentially labeling the samples with different confusion degrees to obtain a label of each piece of data in a to-be-labeled data set. According to the technical scheme, the samples with different confusion degrees are compared and verified through the K trained labeling models, so that the samples with different confusion degrees are automatically labeled, and the labor and time cost is greatly saved.

The invention provides an unmanned aerial vehicle image labeling method assisted by a deep learning target detection algorithm, which belongs to the field of unmanned aerial vehicle image processing. The present invention proposes different schemes for two scenarios of unmanned image labeling: full image labeling scene, using other small amount of public drone image data sets for initial training of detection network, grouping unlabeled images according to the number of targets from less to more After forward inference, automatic processing and manual correction, the sequential input network will add this group of images to the original data set to retrain the network in order to have better detection performance for the next group of images; part of the image area is marked with the scene , the detection network is trained by using sub-images of similar size in the unlabeled area randomly cropped from the labeled area in the dataset, and then the unlabeled area is labeled. The method of the invention greatly reduces the manpower and material resources required for the unmanned aerial vehicle image labeling, and improves the labeling speed and precision.

The invention discloses a movie and television play role labelling method based on search matching. The method comprises the following steps of: obtaining the to-be-labelled object set of a labelling scene and all to-be-labelled object information according to a to-be-labelled object list; constructing a text keyword for each of to-be-labelled objects, and obtaining the corresponding image set by virtue of an image search engine; carrying out face detection and visual attribute analysis on the image of the search result, and removing a noise therein to obtain a role face set which is closely related to the labelling scene, of the to-be-labelled objects; carrying out face detection and tracking on the labelling scene to obtain all face sequences therein; carrying out role labelling on the labelling scene on the basis of a visual similarity among the face sequences, and a visual similarity analysis on the face sequences and the role faces of the to-be-labelled objects. According to the method disclosed by the invention, movie and television play role labelling is carried out by virtue of face images related to movie and television play roles in the Internet; the method disclosed by the invention has the beneficial effects that the labelling process is fully-automatic, high in labelling accuracy, and high in method extensibility and universality.

The invention discloses a correlation feedback method for actively selecting multi-instance multi-label digital images. Multi-instance multi-label learning is a new machine learning framework proposed in recent years, which has been successfully applied in many practical problems. The automatic image annotation technology based on multi-instance multi-label input representation can be well applied to real-world tasks. However, with the enhancement of its expressive ability, the demand for marked training samples of automatic image annotation methods increases sharply as the representation space becomes larger. The present invention combines multi-instance multi-label learning and active learning technology in machine learning, without increasing the cost of user labeling, and obtains more detailed and rich label information in the process of each relevant feedback, thereby improving the system to a greater extent Labeling accuracy effectively reduces the user's participation cost.