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Researches On Visual Saliency Based Video Hashing For Video Copy Detection

Posted on:2014-01-06Degree:MasterType:Thesis
Country:ChinaCandidate:J WangFull Text:PDF
GTID:2248330398960348Subject:Communication and Information System
Abstract/Summary:PDF Full Text Request
With the development of multi-media, the spread of online video becomes very convenient and rapid. The shooting, edit and management of digital video are so easy that thousands of digital video are created everyday. Meanwhile illegal pirates always do some edit towards the video (for example, add noise, add frame, change scale, filtration, picture in picture, add subtitles, JPEG compression, change contrast and many other attack), making pirated video also appear in multiple rapid propagation, which violate the interests of copy owner heavily. With this development, the research of video copy detection technology becomes the hotspot of the fields of multimedia information copyright processing gradually, and come to use in video tracking, video content retrieval, video content authentication, copyright protection and video filtration. So how to build more robust video copy detection system model becomes the key research both at home and abroad.This paper introduces the basic theory of mechanism of video copy detection system firstly; and then introduces a kind of time and space combined hash algorithm used for video copy detection, and on this basis, this paper take the affect of human perception system to video content features into consideration, and introduce human visual attention model. And put forward video copy detection algorithm based on visual attention. They study the application of visual attention model and its analysis in video hash formation and video hash weighted. At last the paper introduces the improvement of the visual attention based video copy detection algorithm and its contribution in recall and precision ratio of video copy detection.The main innovation and contribution of this paper include the following four aspects:(1) Proposed a kind of video copy detection based time and space combined algorithm. This algorithm take that video is a set of a series of time continuous video frame into consideration. It extracts time domain and spatial feature instead of time domain feature or spatial feature only previous. Because of the space distribution of video frame color and image edge information changes owing to brightness changes and block effect, the extract scheme of video content feature is not perfect used color histogram and motion vector characteristic. Here we adopt the order feature of video frame block to extract the fingerprint of video content. And it turns out better performance in the detection.(2) Proposed a kind of video copy detection algorithm based on visual attention. This algorithm fully considers the influence of human visual system to the extracted video content feature, so it adds human attention to video copy detection system model. According to different attention degree of human eyes to video content, it gives different weights to each video. And thus there will be not only one weight per hash bit when do the hash matching. Then the extract and analysis of video content feature will more accord with human perception.(3) Introduced the application of visual attention model in hash formation. Compared to video hash fingerprint was formed directly from extracting the order feature of video frame block previously, the improvement was that compute the binary sequence feature of time domain information representative image and binary sequence feature of visual significant image. And then combining these two binary sequence features so that we can get the final video hash fingerprint. The content fingerprint extracted by this way includes human visual attention. The experiment show that it guarantees the recall ratio and meanwhile improve the precision ratio.(4) Introduced the improvement of video attention model in video copy detection system. In order to improve the recall ratio and precision ratio of video copy detection more, we combine the binary sequence feature of time domain information representative image and binary sequence feature of visual significant image to get a binary sequence of a video clip firstly, and then make use of attention model again, and do block process to representative image according to human eye characteristic. We compute the weight of every block and distribute this weight to the binary sequence of the above video to attain the final hash fingerprint and do hash matching. The stability of the experiment results provides favorable reference value for video copy detection.A novel video hashing algorithm is proposed, which takes account of visual saliency during hash generation. In the proposed algorithm, Experiments on different kinds of videos with different kinds of attacks verify that the proposed algorithm has better performance on robustness and discrimination.
Keywords/Search Tags:video hash fingerprint, video copy detection, order feature, temporal information representation image, visual attention model
PDF Full Text Request
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