Research On Key Technologyies Of Multi-feature Video Copy Detection

With the wide application of video capture devices and the rapid development ofthe Internet technology, video data on the Internet is growing uncontrollably. Videocopy detection method can detect videos with the same content in a large number ofvideos and has great application requirements and broad application prospects in thefield of digital video copyright protection, video management and indexing as well asmedia tracking. Therefore, the Content-based Video Copy Detection has become aresearch hot in the field of multimedia information processing.The existed video copy detection methods have the drawbacks of hugecomputation amount, low recall and precision rate, low robustness, limited applicationdomains and so on. The research of highly efficient video copy detection methods isurgent. Under this background, efficient video copy detection technology is studied inthis paper in the following aspects:1. A method of Video Copy Detection Based on Spatial-tempral Color FeatureCurves(SCFC-VCD) is proposed. This method is proposed to deal with the hugecomputation problem. First, each frame is segmented and average of Y color and Ucolor is computed. Combine corresponding values of average Y and U according tothe frames’ play order to get the video’s color feature curves. Then, the extracted colorfeature curves are matched with those of the target video. In the matching of thefeature curves, in order to remove the impact of luminance and chrominance overallshift, a similarity matching algorithm based on the gradient curves is introduced. Anexception facor is also adopted to remove the impact of abupt interference. To dealwith the matching of videos with different time scales, a method based on improvedDynamic Time Warping is proposed. The experimental results show that SCFC-VCDmethod is small in computation and it is faster than other methods. For videos whosecontent change frequently such as advertisements, the proposed method can detectvideos effectively. It is also robust to common disturbs. For videos whose contenthardly change such as TV series, the proposed method can filter most unrelativevideos quickly which can reduce the computation in the following keyframe-basedprocess.2. A Three-dimentional Quantized Color Histogram (TQCH) method is proposed.Color histogram is sensitive to color changes at quantize edges. TQCH is proposed todeal with this problem. First, HSV color values of keyframes are quantized non-uniformly. Then color histograms are calculated. To decrease the quantized errorat edges, neibor values in H part of histogram is added and the resulting histogram isdefined Three-dimentional Quantized Color Histogram and is used to represent thecolor feature of the keyframe. At last, corresponding matching method is proposed.Experimental results show that TQCH method represent the color featurs of thekeyframe effectively. For commen color images, its recall and precision is higher thanother color-based methods. It is also robust to common disturbs.3. A method of Connected Component Based Affine InvariantRegion(CCB-Affine) is proposed. The existing shape feature extraction methods havedrawbacks of small features, low in repeatness and robustness. A new affine invariantfeature extractor and descriptor is proposed. In the detector, keyframes ispreprocessed and then the pixels with the same grayscal value are connected to form aconnected region. Regions whose gray value difference is smaller than the thresholdare merged. The last merging result is the affine regions. At last, certain methods areused to remove bad regions. In the descriptor,6invariant moments are constructedbased on complex centre moments. Experimental results show that the proposedmethod can detect keyframe shapes effectively and it is also robust to commendisturbs including change in views. It is more robust than other methods and candetect enough shape features.4. A Steerable Pyramid Binary Image Projection (SP-BIP) method is proposed.To get multi-scale and multi-oritation features of the keyframe, SP-BIP is proposed.First, oritation normalization is performed to the grayscale keyframe. The keyframe isperformed Pyramid decomposition. The result sub-images is binarized according theirown thresholds. Then normalized row projection and column projection are computedto represent the texture features. Vector intersect is used to match tow keyframes.Experimental results show that the proposed method can extract multi-scale andmulti-oritation texture features of the keyframe. It is superior to wavelet transformbased method. It is also robust to commen disturbs.5. Tri-training Based Multi-feature Video Copy Detection(TBM-VCD) method isproposed. To fuse different video visual features the new fuse method is proposed.Color feature, shape feature and texture features of videos is effectively fused.Disadvantages of one kind of feature are removed. Through co-training of3calssifiers,video copy detection recall and precision is improved. Experimental results show thatTBM-VCD method has advantages of fastness, high recall and precision and can be used for different kinds of videos. Compared with state-of-the-art methods, it is highin recall and precision and fullfill the needs of video copy detection.