In this paper, we propose a new, effective, and unified scoring method for local feature-based image retrieval. The proposed scoring method is derived by solving the large-scale image retrieval problem as a classification problem with a large number of classes. The resulting proposed score is based on the ratio of the probability density function of an object model to that of a background model, which is efficiently calculated via nearest neighbor density estimation. The proposed method has the following desirable properties: (1) has a sound theoretical basis, (2) is more effective than inverse document frequency-based scoring, (3) is applicable not only to quantized descriptors but also to raw descriptors, and (4) is easy and efficient in terms of calculation and updating. We show the effectiveness of the proposed method empirically by applying it to a standard and improved bag-of-visual words-based framework and a k-nearest neighbor voting framework.

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In this paper, we propose a new, effective, and unified scoring method for local feature-based image retrieval. The proposed scoring method is derived by solving the large-scale image retrieval problem as a classification problem with a large number of classes. The resulting proposed score is based on the ratio of the probability density function of an object model to that of a background model, which is efficiently calculated via nearest neighbor density estimation. The proposed method has the following desirable properties: (1) has a sound theoretical basis, (2) is more effective than inverse document frequency-based scoring, (3) is applicable not only to quantized descriptors but also to raw descriptors, and (4) is easy and efficient in terms of calculation and updating. We show the effectiveness of the proposed method empirically by applying it to a standard and improved bag-of-visual words-based framework and a k-nearest neighbor voting framework.

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