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We tackle the challenge of web image classification using additional tags information. Unlike traditional methods that only use the combination of several low-level features, we try to use semantic concepts to represent images and corresponding tags. At first, we extract the latent topic information by probabilistic latent semantic analysis (pLSA) algorithm, and then use multi-label multiple kernel...
Shape-descriptor (e.g. Adjacent Contour Segments, i.e. kAS) and key point-descriptor (e.g. Scale Invariant Feature Transform, i.e. SIFT) are widely used for computer vision. However, few works principally integrate shape-descriptor and key point-descriptor to describe the content of an image. On one hand, in some cases the degree of locality of keying-descriptor is too high to capture semantic characteristics...
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