Our proposed method to improve the efficiency of BoW is described in Section \[sec:method\]. Section \[sec:results\] presents the experimental results and the evaluation criteria, and the result of our comparisons. Finally, we conclude the paper in Section \[sec:conclud\].

BACKGROUND {#sec:bakgnd}
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Bag-of-Words for image retrieval {#sec:bow}
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Bag-of-words is a popular model that has been used in image classification, objection recognition, and appearance-based navigation. Because of its simplicity and search efficiency it has been used as a successful method in Web search engines for large-scale image and document retrieval [@sivic; @nister; @Sivic2]. Bag-of-words model represents an image by a sparse vector of visual words. Image features, e.g., SIFTs [@lowe], are sampled and clustered (e.g., using k-means) in order to quantize the space into a discrete set of visual words. The centroids of clusters are then considered as visual words which form the visual vocabulary. Once a new image arrives, its local features are extracted and vector-quantized into the visual words. Each word might be weighed by some score which is either the word frequency in the image (i.e., *tf*) or the “term frequency-inverse document frequency” or *tf-idf* [@sivic]. A histogram of weighted visual words, which is typically sparse, is then 