f9b902fc3289af4dd08de5d1de54f68f-Reviews.html

This paper proposes a discriminative clustering algorithm inspired by mean shift and the idea of finding local maxima of the density ratio (ratio of the densities of positive and negative points). The work is motivated by recent approaches of [4,8,16] aimed at discovering distinctive mid-level parts or patches for various recognition tasks. In the authors' own words from the Intro, "The idea is to search for clusters of image patches that are both 1) representative... and 2) visually discriminative. Unfortunately, finding patches that fit these criteria remain rather ad-hoc and poorly understood. While most current algorithms use a discriminative clustering-like procedure, they generally don't optimize elements for these criteria, or do so in an indirect, procedural way that is difficult to analyze. Hence, our goal in this work is to quantify the terms'representative' and'discriminative', and show that that a generalization of the well-known, well-understood Mean-Shift algorithm can produce visual elements that are more representative and discriminative than those of previous approaches."