Abstract
Local interest points serve as elementary building blocks in many image retrieval algorithms, and most of them exploit the local volume features using a Bag of Feature (BOF) model. However, the model ignores seriously valuable information about the global features in image and the distribution of the interest points. In this paper, we combine the sift feature and a global color feature. Then, we propose an improved strategy based on the BOF model. Finally, we embed the binary of the sift and color feature in the BOF model. Convincing experimental results on several datasets demonstrate that our proposed method approaches to the state-of-the-art level in image retrieval.
Published Version
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