IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 __________________________________________________________________________________________ Volume: 03 Special Issue: 07 | May-2014, Available @ http://www.ijret.org 489 VIDEO COPY DETECTION USING SEGMENTATION METHOD AND MPEG-7 DESCRIPTORS Girija K 1 , Sabarinathan P 2 , Saravanan D 3 , Uma M 4 1 PG scholar, Department of Computer Science and Engineering, Pavendar Bharathidasan college of Engg. and Tech., Trichy, Tamilnadu, India 2 Assistant Professor, Department of Computer Science and Engineering, Pavendar Bharathidasan college of Engg. and Tech., Trichy, Tamilnadu, India 3 Associate Professor, Department of Computer Science and Engineering, Pavendar Bharathidasan college of Engg. and Tech., Trichy, Tamilnadu, India 4 Assistant Professor Department of Computer Science and Engineering, Pavendar Bharathidasan college of Engg. and Tech., Trichy, Tamilnadu, India Abstract 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. Keywords: Video copy detection, graph, SIFT feature, SVD, CEDD, FCTH, SCD, EHD, CLD descriptor, -----------------------------------------------------------------------***-------------------------------------------------------------------- 1. INTRODUCTION Now-a-days, server space is becoming a major issue for high level organizations to maintain enormous amount of data. For instance, organizations like YouTube, Google, Metacafe and others dealing with enormous video storage are found to have acres of racks consisting of hard disks each could be holding a capacity of around 1 to 2TB. But we are aware of the fact that most of the videos available in these video storage websites are redundant. According to the recent statistics [14], there are about 27 percent of redundant videos in You Tube, Google videos. Redundant videos are of two types: Copy videos and near duplicate videos. A copy can be defined as a segment of video derived from another video, usually undergone a lot of transformations, such as Cam-cording, PiP (Picture in Picture), Insertions of patterns such as captions, subtitles, logo; Strong re-encoding; Change of gamma; Decrease in quality: Blur, frame dropping, compression, ratio and white noise, Post production is shown in TABLE 1. All of the above specified transformations can be done to a specific original video or else some of these transformations can also be done based on the needs of the one who is implementing these transformations. Both of these above specified cases are considered to be a copy of the original version of that respective video which is available in the dataset maintained by us. Near duplicate videos are the ones representing the same sequence of actions or an event which are recorded by two different cameras from different position or angle. Even though these two videos are representing the same sequence of actions, they are not considered to be copies since they are not edited from an existing original video recorded by someone else. So this paper is not concerned about detecting near duplicate videos. The ultimate goal of video copy detection is to decide whether a video query is copied from a video available in the video dataset. A copy could have undergone various transformations specified earlier. If the system finds the sequence matching results to be the same in the client and server side, the system would prompt with a message saying that the video input of