Abstract
As the usage of multimedia data increasing rapidly, how to get the video data we need efficiently become so important. Recent advances in multimedia technologies allow the capture and storage of video data with relatively inexpensive computers. Problem Statement: However, without appropriate search techniques all these data are hardly usable. Users want to query the content instead of the raw video data. Today research is focused on video retrieval. Content-based search and retrieval of video data becomes a challenging and important problem. To retrieve the content of the video the user need automatic classification and categorization of the visual content. Approach: In this study a novel algorithm is proposed for shot detection using Genetic Edge Histogram and 2-D discreate cosine transform as a feature and multiple features like color, motion, shape and SIFT are used to retrieve the similar shots. Results and Conclusion: The combination of proposed features yields good results interms of precision and recall.
Highlights
Video has a strict hierarchy in nature
Retrieval of multimedia data is mainly divided into two parts:
From the obtained results it is clear that the combination of the proposed features performs well in shot detection and CBVR
Summary
Video has a strict hierarchy in nature. Each video is divided in to scenes and each scene consists of meaning full shots. A shot is defined as one or more frames. Shot represents a continuous action in time or space. A frame has a real world objects in it. Retrieval of multimedia data is mainly divided into two parts:. Text retrieval is an easy process and it has been used for long time. It has so many problems like manual text annotations, selecting a text for process. In content based retrieval spatial features like color, shape, texture and the temporal features like motion can be used to retrieve videos
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