50 results about "Video copy detection" patented technology

Content streams may be segmented to provide automatic extraction and storage of content items without intervening commercials or other unrelated content. These content items may then be stored in a database and made accessible to subscribers through, for example, an on-demand service. Automatic segmentation may include the identification of program boundaries, segmentation of a content stream based on the boundaries and the subsequent classification of the segments into content types. For example, audio and video duplication detection may be used to identify commercials since commercials tend to repeat frequently over a relatively short amount of time. A system may further identify an end of program indicator in a video stream to determine when a program ends. Accordingly, if a program ends after a scheduled end time, a recording device (e.g., the program is being recorded) may automatically extend the recording time to capture the entire program.

A two-step matching technique is embodied in a video-copy-detection algorithm that detects copies of video sequences. The two-step matching technique uses ordinal signatures of frame partitions and their differences from partition mean values. The algorithm of this invention is not only robust to intensity / color variations it can also effectively handle various format conversions, thereby providing robustness regardless of the video dynamics of the frame shots.

The invention belongs to the technical field of the video processing, and particularly relates to a method for identifying content of a digital video, which mainly comprises a key frame extraction module, a video copy detection module and a video frame content identification module. The video copy detection module adopts a video copy detection method based on local ordering to ensure that a similar frame is rapidly obtained through a given possibility, and to improve the detection performance. The video frame content identification module adopts a pornographic image identification algorithm, the algorithm comprises four weak classifiers (skin area proportion classifier, face quantity classifier, invariant-moment classifier and skin area distribution classifier), the four weak classifiers are integrated to form one strong classifier, the result of the strong classifier is the final detection result, so the mistake recognition rate and nonrecognition rate can be effectively reduced. The method has wide application value for the internet digital video information monitoring.

The invention discloses a video copy detection method based on multi-feature Hash, which mainly solves the problem that detection efficiency and detection accuracy cannot be effectively balanced in the exiting video copy detection algorithm. The video copy detection method based on multi-feature Hash comprises the following realization steps of: (1) extracting the pyramid histogram of oriented gradients (PHOG) of a key frame as the global feature of the key frame; (2) extracting a weighted contrast histogram based on scale invariant feature transform (SIFT) of the key frame as the local feature of the key frame; (3) establishing a target function by a similarity-preserving multi-feature Hash learning SPM2H algorithm, and obtaining L Hash functions by optimization solution; (4) mapping the key frame of a database video and the key frame of an inquired video into an L-dimensional Hash code by virtue of the L Hash functions; (5) judging whether the inquired video is the copied video or not through feature matching. The video copy detection method based on multi-feature Hash disclosed by the invention is good in robustness for multiple attacks, and capable of being used for copyright protection, copy control and data mining for digital videos on the Internet.

The invention discloses a system and a method for parallel video copy detection. The method includes the steps of 1, selecting key-frames of a query video and a reference video by a parallel method and extracting MIFT features of the key-frames; 2, clustering extracted feature data of the reference video by a parallel hierarchical clustering method; 3, quantizing the features of the query video and the reference video by a quantitative method according to clustered results; 4, establishing indexes of quantized data of the reference video; and 5, retrieving by the parallel method, preliminarily searching in the indexes to obtain an alternative video by utilizing quantized data of the query video and then computing space consistency and time consistency to finally confirm a copy video. According to the system for parallel video copy detection, a parallel mechanism is adopted on the basis of fast retrieval of visual vocabulary bag model BOF (beginning of file), and accordingly detection efficiency of the system is improved greatly.

A two-step matching technique is embodied in a video-copy-detection algorithm that detects copies of video sequences. The two-step matching technique uses ordinal signatures of frame partitions and their differences from partition mean values. The algorithm of this invention is not only robust to intensity / color variations it can also effectively handle various format conversions, thereby providing robustness regardless of the video dynamics of the frame shots.

The invention discloses a video copying detection method based on robust hash. The advantages of spatial-temporal union information and a sequence characteristic in the aspect of reflecting video contents are used fully. The method comprises the following steps of: firstly, dividing a video frame into parts which are overlapped with one another in 50 percent and generating a time domain information representative image by using the video frame of each part; secondly, filling the time domain information representative images by using a hilbert curve to further calculate a gray scale three-meanvalue of each block on the hilbert curve, and generating a hash fingerprint by comparing the three-mean values; and finally, performing hash matching according to the obtained hash fingerprint and judging whether the video is a copied video. An experimental result shows that the robustness and the discrimination of an extracted characteristic are greatly improved and developed.

The invention provides a video copy detection method and a video copy detection system based on a soft cascade model sensitive to deformation. The method comprises the following steps of: preprocessing, namely extracting vision key frames and audio frames of which the formats are consistent from an inquiry video; identifying deformation, namely distinguishing the type of the deformation of the inquiry video, and transmitting the type information to a cascade detector chain corresponding to the type; and detecting, namely sequentially processing the inquiry video by detectors in the cascade detector chain until a certain detector determines that the inquiry video is a copy or all the detectors determine that the inquiry video is not a copy, wherein in a certain detector, the vision key frames or the audio frames of the inquiry video are searched according to a vision characteristic or an audio characteristic; and then a time domain pyramid trusted platform module (TPM) is used for integrating search results of the frame layers into a copy detection result of a video layer. According to the method and the system, whether the inquiry video is a copy of a given reference video base can be accurately and quickly distinguished; and the method and the system have great significance in application to the fields of digital copyright management, advertisement tracking, video content filtration and the like.

A method for a matching technique for detecting copies of a video clip is provided. The method includes comparing temporal trails of intensity distributions between frames of the video clip and frames of a target video. A computer readable media, a computer system and integrated circuit for video copy detection are also provided.

The invention belongs to the technical field of video images, in particular to a video copying detection method being suitable for IPTV, comprising the following steps: detecting 'picture in picture' of a video; dividing and gathering the video lens; calculating video frame similarity; and constructing video lens index by utilizing a K-means tree and the like. The method of the invention realizes rapid and efficient video copying detection by utilizing a concise and effective video frame similarity processing method and utilizing a K-means tree to construct lens index; and the method for performing the lens detection is not only rapid, but also has good detection accuracy and coverage rate.

The invention belongs to the field of digital media and provides a video copy detection method based on compact video representation. The method comprises following steps: densely extracting keyframes of video library and inquiring video; extracting the image sparse features of the keyframes of the video library and the inquiring video; by means of the pooling mode, the image sparse features of the video library and the inquiring video are merged to form concise video features. The beneficial effects of the invention are: by means of the method, video information can be accurately described; feature data is effectively reduced; the retrieval is accelerated greatly; the method combines deep learning method and traditional method; on the premise of accurate matching, machine performance load is reduced and the problems in the prior art are solved.

The invention provides a video copy extraction method based on tensor decomposition, in particular to a video copy detection method based on multimodal features and tensor decomposition, which includes the following steps: (1) video preprocessing: video clips are standardized by spatial-temporal sampling; (2) video tensor modeling and tensor decomposition: the global, local and time domain features of the video are respectively extracted, tensor modeling is carried out, and the Tucker model is utilized to carry out tensor decomposition, so that a nuclear tensor and a low-order tensor are obtained; (3) video fingerprint matching: the nuclear tensor is utilized to carry out rough matching, and a video fingerprint is utilized to carry out fine matching in a coarse selection. Compared with the prior art, the method realizes the true complementary fusion of multimodal features of a video, not only overcomes the defect of poor robustness of video fingerprints constructed with single-mode features, but also realizes the temporal associated co-occurrence between a variety of modes of features, and increases the accuracy and efficiency of video copy extraction.

The invention is applicable to the technical field of multimedia information processing, and provides a video copy detection method and system thereof. The method comprises the following steps: obtaining an image tag Y in the image training set; carrying out copy attack on each image tag Y to obtain a first image X. The training model of depth feature extraction model is constructed, and the depthfeature extraction model is obtained by training. Obtaining a first fusion feature after fusing the first depth feature and the first DCT coefficient feature of each second image, and storing the first fusion feature as a fusion feature database; Extracting a third key frame of the query video, and fusing the second depth feature and the second DCT coefficient feature to obtain a second fusion feature; Each of the second fusion features is matched with each of the first fusion features to obtain a detection result of the query video. Thus, the invention improves the discrimination and robustness of the video features and improves the accuracy of the video copy detection system.

The invention discloses a video copy detection method based on the deep learning and graph theory, and aims to improve the speed and accuracy of image frame similarity matching. The technical scheme is to extract an image frame in a reference library video, extracting features of the image frame, and obtaining a feature matrix of the image frame in the reference library video; extracting an image frame in the video to be detected, extracting features of the image frame, and obtaining a feature matrix of the image frame in the video to be detected; performing similarity matching between the feature matrix of the image frame in the video to be detected and the feature matrix of the image frame in the reference library video by using an approximate nearest neighbor search algorithm, and obtaining a similarity matching list; and judging and locating video copy fragments by means of the graph theory according to the similarity matching list. The method of the invention can be used to cope with various video editing transformations and an arbitrary matching form between the video to be detected and the reference library video, solve the technical problem that the "noise" has a great influence on the matching effect and improve the speed and accuracy of the similarity matching of the image frames.

The invention belongs to the technical field of computer video processing and particularly discloses a video copying detection method for advertisement detection. The video copying detection method for the advertisement detection comprises firstly detecting candidate matching advertisement fragments through an audio matching method, then verifying the candidate matching advertisement fragments through an image matching method and achieving video copying detection. The video copying detection method for the advertisement detection has the advantages of being efficient due to an algorithm which combines image and audio features and enabling large-sized video to be processed and being rapid in detection process and accurate in result.

The invention provides a video copy detection method based on improved OM features. The method comprises the steps that the improved OM features are extracted from a source video data set, and an index is established; the improved OM features are extracted from a query video, a source video most similar to the query video is calculated based on the index, and when the similarity value is larger than a set value, the source video is selected as a candidate source video; the number of sequence matching key frames between the candidate source video and the query video is calculated, whether the query video is a copy of the candidate source video or not is judged according to a threshold value, and the copied segment in the source video is positioned. According to the method, the spatial feature of a video frame image and the brightness change between video frames are taken into full consideration, and the spatial feature and the brightness change promote each other, so that video content is described more accurately, and consequently a higher accuracy rate of video copy detection is obtained.

The invention discloses a video copy detection system based on a convolutional and recurrent neural network. The system includes five modules, which are a data set establishment module, a frame feature extraction module, a spatio-temporal feature training module, a loop network test module and a copy video matching module. The spatio-temporal feature training module also includes video editing module and loop network training module. The invention adopts a residual convolutional neural network to extract deeper frame-level feature representationto improve the detection accuracy and reduce thedetection recall effectively. A twin-loop neural network is used to fuse multiple frame-level features, and the spatio-temporal feature representation is generated by using the dynamic information between frames, so the spatio-temporal fusion between sequences is realized, and video matching takes less time and occupies less memory.

The invention relates to an audio / video (AV) copy detection method and device. The AV copy detection method includes: acquiring an AV image, performing decoding and pretreatment on the AV image, and performing feature extraction on the acquired audio part and the acquired video frames to obtain corresponding audio features and corresponding image features of the video frames; fusing the audio features and the image features of the video frames corresponding to the AV image to obtain AV fusion features; matching the AV fusion features based on a preset feature library of a reference video to obtain a frame set matching result; and performing copy determination and positioning on the AV image based on the frame set matching result and the reference video. Through the method for fusing the audio and the video, the robustness of the video copy detection system can be enhanced; the audio features and the video features are fused, the execution efficiency of the copy detection system can be greatly improved; and the audio and the video are analyzed at the same time, and then the positioning accuracy of copy segments can be improved.

The invention provides a video copy detection method based on time-domain visual attention. The method comprises the following steps of: acquiring visual attention changes between different video frames according to a visual attention mechanism, and acquiring representation of time-domain attention degree; calculating a time-domain attention weight of a video frame in a video clip according to the time-domain attention degree to form a visual attention transfer image of the video clip; and finally, extracting video hash on the generated visual attention transfer image provided with time domain and space domain information. By the method, the time domain information of the video is fully considered, a video frame which makes the video content prominent is weighted, the time domain and space domain information is integrated through the extracted characteristics, and the video copy detection method has high robustness on time domain attacks.

The invention discloses a deep feature fusion video copy detection method based on an attention mechanism, and the method comprises the steps: (1) extracting a frame image from video data, and constructing an image pyramid through employing different scales; (2) taking the deep convolutional neural network model as a basic network, and adding an attention mechanism into a middle convolutional layer of the deep convolutional neural network model; (3) inputting the frame image and the image pyramid into a deep convolutional neural network model of an attention mechanism, and obtaining fusion features through splicing and fusion; (4) training a deep convolutional neural network model in a metric learning mode; and (5) obtaining source video data through similarity calculation by utilizing thetrained deep convolutional neural network model. According to the method, an attention mechanism and fusion of global features and local features are utilized, the problems that a traditional image processing method is low in efficiency and low in precision can be solved, and the problem that the global features cannot adapt to various complex changes can also be solved.

The invention discloses a video fingerprint extraction method based on slowly-changing visual features. The method comprises: For each image in a training set, an image of a random distortion versionis generated, spatial features are trained by using an original image and a distortion image, and parameters of a network are extracted; spatial and sequential normalization preprocessing is carried out on video data of the training set, wherein the data are processed into ones with a fixed dimension and a fixed frame number; a feature sequence of the network is extracted based on the spatial features and the extracted sequence is used as training data and an LSTM network is trained; and a network is extracted from the trained spatial features, the obtained features are in cascade connection with the LSTM network, and a video fingerprint is extracted. According to the method disclosed by the invention, the visual perception principle of the human being is simulated and thus the video fingerprint can be extracted. The method has the high robustness and high efficiency. The video fingerprint extraction method can be applied to fields of video copy detection and video retrieval and the like.

A method for a matching technique for detecting copies of a video clip is provided. The method includes comparing temporal trails of intensity distributions between frames of the video clip and frames of a target video. A computer readable media, a computer system and integrated circuit for video copy detection are also provided.

The invention provides a quadruple convolutional neural network video fingerprint algorithm. The algorithm comprises the steps of establishing a projection excitation network; constructing a quadrupleconvolutional neural network video fingerprint algorithm according to the projection excitation network; through video data selection, inputting the constructed quadruple video sequence into a quadruple convolutional neural network to carry out training and performance testing of the quadruple convolutional neural network. According to the invention, mapping from original video data to discrete binary codes can be realized in an end-to-end manner; simplified algorithm complexity, during training, tetrad loss and quantization error loss are used for jointly optimizing network parameters; on one hand, the intra-class variance is reduced and the inter-class variance is increased due to the loss of the tetrad; and on the other hand, the quantization error loss can reduce the loss of semanticsimilar information in a real value feature binarization process, the precision ratio and recall ratio of the method in the aspect of video copy detection are obviously improved, and the obtained video fingerprint can maintain relatively high robustness and uniqueness while meeting compactness.

The invention discloses a method and system for identifying harmful videos. The method comprises the steps of when it is judged that the page elements of a webpage comprise the URL path of the video,identifying an IP address or an IP address field of a user recorded in the page content of the webpage, acquiring a domain name contained in the URL or an IP address pointed by the URL according to the URL path of a video, and outputting a first weight factor and a second weight factor based on the related query of the IP address and the domain name; obtaining a video file at the lowest picture quality, and carrying out the video copy detection on the video file of the lowest picture quality in a pre-established harmful video database, and outputting a third weight factor according to a monitoring result; and combining the first weight factor, the second weight factor and the third weight factor to identify whether the video belongs to the harmful video. According to the method and the system, by combining a database made by the big data, using the image processing means as few as possible and utilizing multiple modes, a scheme for identifying the harmful videos is provided.

The invention relates to the field of video classification and provides an end-to-end video copy detection method based on deep learning, which aims to solve problems in the video copy detection, suchas it is difficult to detect multiple copy segments in two pieces of videos, positions of copy video segments cannot be accurately positioned and the like. The specific embodiment of the method includes the following steps: performing shot segmentation on two video segments to be detected which are used for video copy detection, so as to select key frames; recognizing selected a plurality of keyframes by using a pre-built copy relationship recognizing model, and determining a copy relationship among the key frames; constructing a copy relationship matrix of all the key frames of two segmentsof the to-be-detected videos, according to the obtained copy relationship among the key frames; using the copy relationship matrix as an input of the pre-built positioning recognition model, and positioning fragments that contain a copy relationship in the two segments of the to-be-detected videos. The end-to-end video copy detection method and device based on deep learning can quickly and efficiently detect the video fragments that have the copy relationship in the two segments of the to-be-detected videos.

The invention provides a video copy detection method and device, a storage medium and an electronic device. The method comprises the following steps: extracting a first target key frame of a query video; extracting a first video feature of the first target key frame; acquiring a second video feature of a second target key frame of the reference video; and when it is determined that the query video is matched with the reference video according to the first video feature and the second video feature, extracting similar video clips in the query video and the reference video. The problems of low video copy detection efficiency and poor video copy detection accuracy in a video copy detection method in the related technology are solved, and the effects of improving the video copy detection efficiency and improving the video copy detection accuracy are further achieved.

The invention belongs to the technical field of the video processing, and particularly relates to a method for identifying content of a digital video, which mainly comprises a key frame extraction module, a video copy detection module and a video frame content identification module. The video copy detection module adopts a video copy detection method based on local ordering to ensure that a similar frame is rapidly obtained through a given possibility, and to improve the detection performance. The video frame content identification module adopts a pornographic image identification algorithm, the algorithm comprises four weak classifiers (skin area proportion classifier, face quantity classifier, invariant-moment classifier and skin area distribution classifier), the four weak classifiers are integrated to form one strong classifier, the result of the strong classifier is the final detection result, so the mistake recognition rate and nonrecognition rate can be effectively reduced. Themethod has wide application value for the internet digital video information monitoring.

The invention discloses a method and system for identifying a harmful video. The method comprises the following steps of when it is judged that the page elements of the webpage comprise the URL path of the video, identifying a user ID recorded in the page content of the webpage, acquiring a domain name contained in the URL or an IP address pointed by the URL according to a URL path of a video, andoutputting a first weight factor and a second weight factor based on the user ID, the IP address and a related query of the domain name; moreover, obtaining the video content of the film tail, carrying out the video copy detection on the video content of the film tail in a preset harmful video database, and outputting a third weight factor according to a monitoring result; and combining the firstweight factor, the second weight factor and the third weight factor to identify whether the video belongs to the harmful video. According to the method and the system, by combining a database made bythe big data, using the image processing means as few as possible and utilizing multiple modes, a scheme for identifying the harmful videos is provided.

The invention discloses a video copy detection method based on depth local features, which comprises the following steps: (1) extracting frame images from video data, and then constructing an image pyramid by using different scales; (2) constructing a deep convolutional neural network model for extracting a feature map from the input image pyramid and performing feature fusion on the feature map to obtain a fused feature map; (3) training the deep convolutional neural network model in a metric learning mode; (4) extracting a fusion feature map from an image pyramid by using the trained deep convolutional neural network model; (5) extracting key points from the fusion feature map by using maximum suppression, and extracting corresponding local features according to the key points; and (6) performing video copy detection according to the local features. According to the method, the extraction speed is higher, and the local feature representation is stronger, so that accurate detection can be carried out on various complex transformed copy videos and local features, and the method has the characteristic of high robustness.