An Alternative to IDF: Effective Scoring for Accurate Image Retrieval with Non-Parametric Density Ratio Estimation

作者：Uchida Yusuke^*; Takagi Koichi; Sakazawa Shigeyuki

来源：21st International Conference on Pattern Recognition (ICPR), 2012-11-11 to 2012-11-15.

In this paper, we propose a new scoring method for local feature-based image retrieval. The proposed score is based on the ratio of the probability density function of an object model to that of background model, which is efficiently calculated via nearest neighbor density estimation. The proposed method has the following desirable properties: (1) a sound theoretical basis, (2) effectiveness than IDF scoring, (3) applicability not only to quantized descriptors but also to raw descriptors, and (4) ease and efficiency of calculation and updating. We show the effectiveness of the proposed method empirically by applying it to a bag-of-visual words-based framework and a k-nearest neighbor voting framework.

• 出版日期2012