Copy frame detection in video using data mining techniques

There are a number of methods available for video copy detection. Some of the methods were employing the application of local and global descriptors which were found to be ineffective in detections involving complex transformations. In order to overcome the above specified inefficiency, Scale Invariant Feature Transform (SIFT) descriptor came into picture but was found to have a high computational cost. The method proposed in this paper involving five different types of MPEG-7 descriptors namely Color and Edge Directivity Descriptor (CEDD), Fuzzy Color and Texture Histogram (FCTH), Scalable Color Descriptor (SCD), Edge Histogram Descriptor (EHD), Color Layout Descriptor (CLD) for extracting the features of the frames in the selected video is found to be cost effective and efficient even in case of high level of transformations. This paper also throws light on certain improvements in graph-based video sequence matching method which is used to overcome the level of noise, to detect videos with different frame rates and optimal sequence matching is found automatically from the disordered video sequences by applying spatial features during copy detection. Experimental results have showed that the proposed method is far effective than the previously existing video detection scenarios.