Abstract
In model of image-retrieval based on bag-of-features, a large number of information is lost during the process of quantizing the high-dimensional SIFT features as visual words. It leads to the teeming of errors matching feature points. To address this problem, this paper couples the color information into the BoF model as a complement to sift features, two different features are extracted to represent the same interest region and a visual pair is obtained by quantizing the descriptor pair with two independent codebooks. What’s more, we weight similarity degree of images by using the saliency area of image to decrease the semantic gap between low-level features and high-level expressions. We show that such an approach can significantly outperform matching results compared with traditional model of bag-of-features on Holiday dataset. Furthermore, our strategy of weighting based saliency further improved our performance in image-retrieval.
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