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Fixture selection by maximization of texture and contrast information
R.G. Raj, A.C. Bovik and L.K. Cormack
IEEE International Conference on Image Processing
Abstract
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 texturebased
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).
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