NDLI: A Segmentation Based Sequential Pattern Matching for Efficient Video Copy Detection

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A Segmentation Based Sequential Pattern Matching for Efficient Video Copy Detection

Content Provider	CiteSeerX
Author	Shinde, Sanket Chiddarwar, Girija
Abstract	Abstract—We propose a video copy detection system which is sufficiently robust to detect transformed versions of original videos with ability to pinpoint location of copied segments. Precisely due to the good stability and discriminative capability, SIFT feature is used for visual content description. However SIFT based feature matching has high computational cost due to large number of keypoints and high dimensionality of its feature vector. Thus to minimize computational complexity, video content is divided into number of segments depending on homogeneity of video content. SIFT features are extracted from keyframes of video segments. Then SIFT features are quantized to generate clusters. Further binary SIFT are generated for every cluster for optimized matching. To perform video segment matching with SIFT descriptors, firstly visual words matching is done at cluster level then at feature level, similarity measure is employed. In order to find video copy detection, an optimal sequential pattern matching is done which will ensure effectiveness and efficiency of the proposed video copy detection system. Our video copy detection system gives improved results in terms of accuracy and has been able to detect most of the dataset query video transformations in time-efficient manner.
File Format	PDF
Access Restriction	Open
Subject Keyword	Efficient Video Copy Detection Sift Feature Sequential Pattern Matching Video Copy Detection System Video Segment Video Content Feature Level Sift Descriptor Original Video Visual Content Description High Dimensionality Good Stability Dataset Query Video Transformation Transformed Version Discriminative Capability Video Copy Detection Optimized Matching High Computational Cost Copied Segment Visual Word Optimal Sequential Pattern Matching Feature Matching Computational Complexity Feature Vector Similarity Measure Improved Result Cluster Level Time-efficient Manner Binary Sift
Content Type	Text

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