An efficient key point quantization algorithm for large scale image retrieval

We focus on the problem of large-scale near duplicate image retrieval. Recent studies have shown that local image features, often referred to as key points, are effective for near duplicate image retrieval. The most popular approach for key point based image matching is the clustering-based bag-of-words model. It maps each key point to a visual word in a code-book that is constructed by a clustering algorithm, and represents each image by a histogram of visual words. Despite its success, there are two main shortcomings of the clustering-based bag-of-words model: (i) it is computationally expensive to cluster millions of key points into thousands of visual words; (ii) there is no theoretical analysis on the performance of the bag-of-words model. We propose a new scheme for key point quantization that addresses these shortcomings. Instead of clustering, the proposed scheme quantizes each key point into a binary vector using a collection of randomly generated hyper-spheres, and a bag-of-words model is constructed based on such randomized quantization. Our theoretical analysis shows that the resulting image similarity provides an upper bound for the similarity based on the optimal partial matching between two sets of key points. Empirical study on a database of 100,000 images shows that the proposed scheme is not only more efficient but also more effective than the clustering-based approach for near duplicate image retrieval.