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This paper presents a new framework for visual bag-of-words (BOW) refinementand reduction to overcome the drawbacks associated with the visual BOW modelwhich has been widely used for image classification. Although very influentialin the literature, the traditional visual BOW model has two distinct drawbacks.Firstly, for efficiency purposes, the visual vocabulary is commonly constructedby directly clustering the low-level visual feature vectors extracted fromlocal keypoints, without considering the high-level semantics of images. Thatis, the visual BOW model still suffers from the semantic gap, and thus may leadto significant performance degradation in more challenging tasks (e.g. socialimage classification). Secondly, typically thousands of visual words aregenerated to obtain better performance on a relatively large image dataset. Dueto such large vocabulary size, the subsequent image classification may takesheer amount of time. To overcome the first drawback, we develop a graph-basedmethod for visual BOW refinement by exploiting the tags (easy to accessalthough noisy) of social images. More notably, for efficient imageclassification, we further reduce the refined visual BOW model to a muchsmaller size through semantic spectral clustering. Extensive experimentalresults show the promising performance of the proposed framework for visual BOWrefinement and reduction.

Image classification by visual bag-of-words refinement and reduction