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A structured learning framework for content-based image indexing and visual query
2005
Multimedia Systems
Nonspecific images in a broad domain remain a challenge for content-based image retrieval. As a typical example, consumer photos exhibit highly varied content, diverse resolutions, and inconsistent quality. The objects are usually ill-posed, occluded, and cluttered with poor lighting, focus, and exposure. Traditional image retrieval approaches face many obstacles such as semantic description of images, robust semantic object segmentation, small sampling problem, semantic gaps between low-level
doi:10.1007/s00530-004-0158-z
fatcat:z4te2phe4randf4zuiesrpmbgm