Abstract：Concerning the problem that too large visual dictionary may increase the time cost of image classification in the Bag-Of-Visual words (BOV) model, a Weighted-Maximal Relevance-Minimal Semantic similarity (W-MR-MS) criterion was proposed to optimize visual dictionary. Firstly, the Scale Invariant Feature Transform (SIFT) features of images were extracted, and the K-Means algorithm was used to generate an original visual dictionary. Secondly, the correlation between visual words and image categories and semantic similarity among visual words were calculated, and a weighted parameter was introduced to measure the importance of the correlation and the semantic similarity in image classification. Finally, based on the weighing result, the visual word which correlation with image categories was weak and semantic similarity among visual words was high was removed, which achieved the purpose of optimizing the visual dictionary. The experimental results show that the classification precision of the proposed method is 5.30% higher than that of the traditional K-Means algorithm under the same visual dictionary scale; the time cost of the proposed method is reduced by 32.18% compared with the traditional K-Means algorithm under the same classification precision. Therefore, the proposed method has high classification efficiency and it is suitable for image classification.
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2017, Vol. 37 
