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Manual tagging has an important impact to performance of image/video searching by keyword. However, users usually mark tags only landmarks are as on only a few images in library and leave most contents untagged. If landmarks from different places are look alike, it is hard to distinguish even though surroundings are totally different. Rather than using only manual tags of highlight landmark, we proposed...
Local feature analysis of visual content, namely using Scale Invariant Feature Transform (SIFT) descriptors, have been deployed in the ‘bag-of-visual words’ model (BVW) as an effective method to represent visual content information and to enhance its classification and retrieval. The key contributions of this paper are first, a novel approach for visual words construction which takes physically spatial...
This paper presents a novel framework for visual content interpretation and aims to generate meaningful descriptions for visual content based on the aggregate information of the detected primitive objects, spatial relations, and specific relevant prior knowledge to aid visual content interpretation. The main contributions of this paper include: (1) a novel approach to generate semantic descriptions...
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