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An image could be described with local features like SIFT and with those features, images could be represented as “Bag-of-Visual-Words” (BVW). This representation has been widely used in content based image retrieval. Comparing BVW of two images is usually done in Euclidean space, like Euclidean distance or weighted variants. Neither of these methods consider the inter cluster relations. If there...
Video summarization is an efficient and flexible way to represent video data. In this paper, we use the kernel PCA and clustering based key frame extraction to realize multilevel video representation. In order to remove the redundancy caused by large scene changes, SIFT flow scene alignment is performed on the clustering set of key frames. After alignment, one representative frame is chosen from the...
High-precision localization is one of the important applications in the field of computer vision. In this paper a high-precision template localization algorithm based on SIFT (scale invariant feature transform) is presented. The proposed method is composed of three main steps. In the initial step the SIFT features are extracted. With these features the basic matching strategy and clustering method...
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