Research And Implementation On Video Copy Detection Based On SIFT Features

Content-Based Video Copy Detection (CBCD) is an effective means of digital videocopyright protection. The core issue of CBCD is the video feature extraction and matchingalgorithms. The better robustness and distinguishability of video feature will be more conduciveto the accurate expression of the video, which can further improve the effect of video copydetection. Under the keyframe matching framework, the current video copy detection inuncompressed and compressed domain methods failed to make full use of time and spatialdomain information of the video data, which have the problems of poor robustness anddistinguishability of the video feature. Meanwhile, video copy detection based on local-featureshas the disadvantage of high computational complexity. This dissertation conducts specialstudies to the above problems.SIFT features is the acknowledged image local-features which has better performancecurrently. Beginning with the analysis of spatial and temporal characteristics of consecutivevideo frames SIFT features, the dissertation proposes a method of spatiotemporal SIFT featuresvideo copy detection based on the uncompressed domain, which aiming at the problem of poorrobustness and distinguishability of the video feature; aiming at the disadvantage of highcomputational complexity, the dissertation proposes a method of spatiotemporal SIFT featuresvideo copy detection based on the DCT domain; designs a DCT domain and uncompresseddomain cascaded video copy detection system, which realizes the combination of high detectionprecision and speed.The main innovations include:1) A method of spatiotemporal SIFT features and its corresponding video copy detectionmethod based on the uncompressed domain are proposed. On the basis of continuous videosequence analysis of the spatial and temporal characteristics of the SIFT features, thisdissertation proposes technique of shot detection based on SIFT features and method of videofeature extraction by “local convergence, global alienation” strategy, and realizes spatiotemporalSIFT video copy detection in uncompressed domain. Experiments show that compared with thecurrently more popular OM method, the method this dissertation proposed has higher detectionaccuracy.2) A DCT domain reduced image SIFT feature extraction algorithm is proposed, and itscorresponding DCT domain video copy detection is realized. By reducing DCT coefficients ofimage, this dissertation extracts SIFT features of the DCT domain reduced image and proposesthe mothed of spatiotemporal SIFT video copy detection in DCT domain. Experiments show thatthis method can significantly improve the speed of the video copy detection at the cost of acertain loss of detection accuracy.3) A cascade video copy detection system based on DCT and uncompressed domain isdesigned. Because of higher detection speed in DCT domain and higher precision in spacedomain, this dissertation designs the DCT and uncompressed domain cascaded video copydetection system, which has the functions of primary filtrating and secondary deep detection. Also matching-databases is optimized by “Priority Index of Feedback”. Experiments show thatthe cascaded system realizes the complementary advantages of DCT and uncompressed domainmethod, with which higher detection accuracy and lower detection time.