15 January 1997 Semantic image retrieval through human subject segmentation and characterization
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Video databases can be searched for visual content by searching over automatically extracted key frames rather than the complete video sequence. Many video materials used in the humanities and social sciences contain a preponderance of shots of people. In this paper, we describe our work in semantic image retrieval of person-rich scenes (key frames) for video databases and libraries. We use an approach called retrieval through segmentation. A key-frame image is first segmented into human subjects and background. We developed a specialized segmentation technique that utilizes both human flesh-tone detection and contour analysis. Experimental results show that this technique can effectively segment images in a low time complexity. Once the image has been segmented, we can then extract features or pose queries about both the people and the background. We propose a retrieval framework that is based on the segmentation results and the extracted features of people and background.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yanbing Li, Yanbing Li, Bo Tao, Bo Tao, Shun Kei, Shun Kei, Wayne H. Wolf, Wayne H. Wolf, } "Semantic image retrieval through human subject segmentation and characterization", Proc. SPIE 3022, Storage and Retrieval for Image and Video Databases V, (15 January 1997); doi: 10.1117/12.263422; https://doi.org/10.1117/12.263422

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