Publication

Abstract : With the recent development in multimedia technologies, in rapid conjunction with the increase of the volume of digital video data through internet and web technologies. For this purpose solely, content based video retrieval (CBVR) has become a wide and vast area of research throughout the last decade. The objective of this thesis is to present applications for temporal video frame analysis based on performance evaluation of key frames and video sequence from the extracted key frames retrieval based on different mathematical models. In this work, through performance analysis, we extracted the key frames from a video into its constituent units. This is achieved by identifying transitions between adjacent temporal features. The proposed algorithm aims to extract the key frames based on the validation measures and cross mutation function through the modified differential evaluation algorithm. Given the size of the vector containing image pixels, it can be modeled by a parameter based cross evaluation function of the parent vector. The proposed system, designed for extracting key frames, has led to reliable algorithm achieving high performance for object re-identification. In addition, the high computational time allows for key frame analysis in real time. In our research, we opted for a global method based on local optimization. The proposed methodology is being validated against various state of the art key frame extraction algorithm which proves this methodology as reliable and faster complexion process for object re-identification.