In general, video is too much lengthy for browsing the contents. So, there are many efforts being made for browsing the content of the video fast and effectively. Video summary is the one of techniques related to those efforts. Video summary comprises a number of key-frames. Therefore, we propose a method to extract key-frames from the video in MPEG compressed domain. Proposed method extracts the simple 2D content curve reflecting the variation of the video content from the MPEG video in the compressed domain, approximates the curve to polygonal lines and then extracts key-frames from the approximated lines effectively and rapidly. Also, proposed method let the user set the number of key-frames.
In this paper, we propose a new content-based indexing algorithm that utilizes pixel-wise entropy and extracts features such as color and entropy from an image as indices. We propose a technique that fulfills both global and regional searching. Global searching scheme utilizes entropy features with multilevel-multiresolution. As resolution of the image is reduced, another information of the image is revealed. As gray-level of the image is reduced, we see how large the gray- level differences are between neighboring pixels. Regional searching utilizes color features that are extracted from regions separated by entropy measures. Our algorithm provides not only the automated extraction of entropy-based regions but also the representation of their color contents. Thus, we can classify images using entropy and multiresolution multi-level based features. Various experiments show the promising future of the proposed algorithm.
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