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Multimodal classification of remote sensing images: A review and future directions,” (2015)

by G Luis, D Tuia, G Moser, C Gustau
Venue:Proc. of IEEE,
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Efficient Video Retrieval Scheme with Luminance Projection Model

by Sang Hyun Kim , 휘도투시모델을 적용한 , 효율적인 비디오 , 검색기법 김상현 , 2015
"... Abstract A number of video indexing and retrieval algorithms have been proposed to manage large video databases efficiently. The video similarity measure is one of most important technical factor for video content management system. In this paper, we propose the luminance characteristics model to m ..."
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Abstract A number of video indexing and retrieval algorithms have been proposed to manage large video databases efficiently. The video similarity measure is one of most important technical factor for video content management system. In this paper, we propose the luminance characteristics model to measure the video similarity efficiently. Most algorithms for video indexing have been commonly used histograms, edges, or motion features, whereas in this paper, the proposed algorithm is employed an efficient similarity measure using the luminance projection. To index the video sequences effectively and to reduce the computational complexity, we calculate video similarity using the key frames extracted by the cumulative measure, and compare the set of key frames using the modified Hausdorff distance. Experimental results show that the proposed luminance projection model yields the remarkable improved accuracy and performance than the conventional algorithm such as the histogram comparison method, with the low computational complexity.
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...ing may show the low accuracy in the compressed domain [1], which leads to false or miss segmentation. In this paper, to improve the 한국산학기술학회논문지 제16권 제12호, 2015 8650 accuracy and performance of video indexing and segmentation, we propose the efficient method using the luminance projection, which yields a higher performance than the conventional method. The key frames extracted from segmented video shots can be used not only for video shot clustering, but also for video sequence matching or browsing. The key frame is defined as the frame that is significantly different from the previous frames [2]. The key frames can be extracted by employing similar methods used in shot boundary detection with proper similarity measures, and several algorithms have been proposed. The key frame extraction method using set theory employing the semi-Hausdorff distance and key frame selection using skin-color and face detection have been also proposed [3]. In this paper, we propose the efficient algorithm to extract key frames using the cumulative measure and compare its performance with that of the conventional algorithm. Video sequence matching using key frames extracted from each shot can be performed ...

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