Fixation selection by maximization of texure and contrast information

• Published in: 2008 15th IEEE International Conference on Image Processing pp. 697 - 700
• Main Authors: Raj, R.G., Bovik, A.C., Cormack, L.K.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2008
• Subjects: Computational geometry; Contrast; Data mining; Fixation Selection; Humans; Image coding; Image texture analysis; Layout; MICA; Natural Scene Statistics; Non-stationarity; Pattern matching; Performance evaluation; Statistics; Textures; Visual system
• ISBN: 9781424417650; 1424417651
• ISSN: 1522-4880; 2381-8549
• DOI: 10.1109/ICIP.2008.4711850

We present information-theoretic underpinnings of a computation theory of low-level visual fixations in natural images. In continuation of our prior work on optimal contrast-based fixations [1], we develop an optimum texture- based fixation selection algorithm based on a recent theory of non-stationarity measurement in natural images [2]. Thereafter we propose a simple coupling of the optimal texture-based and contrast-based fixation features to produce a new algorithm called CONTEXT, which exhibits robust performance for fixation selection in natural images. The performance of the fixation algorithms are evaluated for natural images by comparison to randomized fixation strategies via actual human fixations performed on the images. The fixation patterns obtained outperform randomized, GAFFE-based [3], and Itti [4] fixation strategies in terms of matching human fixation patterns. These results also demonstrate the important role that contrast and textural information play in low-level visual processes in the Human Visual System (HVS).