An optimized key frame extraction for detection of near duplicates in content based video retrieval

Due to the availability of increased Internet bandwidth and transfer media, the multimedia objects usage is widely used. In multimedia the text files, scripts, images and videos, audios are used by almost every end of users. Daily around millions of videos are being uploaded through various sources. It is becoming very tedious to manage with these multimedia objects specially related to videos. It needs some kind of retrieval technique which will relieve users from tracking videos relatively similar manually. So users are shifted from text based video retrieval to content based video retrieval (CBVR). CBVR has steps as key frame extraction, feature vector formation, similarity and template matching and finally get the retrieved relatively correct expected copies of videos approximately matching with query video. Key frame extraction play very vital role in whole process of CBVR as it decides the search region for search engines affecting the performance. In videos large number of frames forms a scene. This scene includes repetition of nearly same frames with slight differences, which increases the storage space and decreases the performance in video processing. Here instead of searching the whole set of frames in videos, only selected key frame are used for further processing. Automated extraction of key frames has recently gained momentum in the field of video content summarization. Key frames are extracted by computing the consecutive frame differences. Then their relative neighboring frames which nearly match with the key frames are taken aside as near duplicate copies. Therefore the near duplicate copies detection may help to improve the performance and speed of near duplicate detection in content based video retrieval.