Analysis of Sampling Techniques for Learning Binarized Statistical Image Features Using Fixations and Salience

Abstract

This paper studies the role of different sampling techniques in the process of learning Binarized Statistical Image Features (BSIF). It considers various sampling approaches including random sampling and selective sampling. The selective sampling utilizes either human eye tracking data or artificially generated fixations. To generate artificial fixations, this paper exploits salience models which apply to key point localization. Therefore, it proposes a framework grounded on the hypothesis that the most salient point conveys important information. Furthermore, it investigates possible performance gain by training BSIF filters on class specific data. To summarize, the contribution of this paper are as follows: 1) it studies different sampling strategies to learn BSIF filters, 2) it employs human fixations in the design of a binary operator, 3) it proposes an attention model to replicate human fixations, and 4) it studies the performance of learning application specific BSIF filters using attention modeling.