Document Type

Conference Proceeding

Faculty

Faculty of Computing, Health and Science

School

School of Engineering / Systems and Intervention Research Centre for Health

RAS ID

14749

Comments

This article was originally published as: Ngo, A., Qiu, G., Underwood, G., Ang, L. K., & Seng, K. (2012). Visual Saliency Based on Fast Nonparametric Multidimensional Entropy Estimation. Proceedings of 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). (pp. 1305-1308). Kyoto, Japan. IEEE. Original article available here

© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Abstract

Bottom-up visual saliency can be computed through information theoretic models but existing methods face significant computational challenges. Whilst nonparametric methods suffer from the curse of dimensionality problem and are computationally expensive, parametric approaches have the difficulty of determining the shape parameters of the distribution models. This paper makes two contributions to information theoretic based visual saliency models. First, we formulate visual saliency as center surround conditional entropy which gives a direct and intuitive interpretation of the center surround mechanism under the information theoretic framework. Second, and more importantly, we introduce a fast nonparametric multidimensional entropy estimation solution to make information theoretic-based saliency models computationally tractable and practicable in realtime applications. We present experimental results on publicly available eyetracking image databases to demonstrate that the proposed method is competitive to state of the art.

DOI

10.1109/ICASSP.2012.6288129

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