Selecting salient objects in real scenes: An oscillatory correlation model

Authors:
Marcos G. Quiles;DeLiang Wang;Liang Zhao;Roseli A. F. Romero;De-Shuang Huang
Affiliations:
Department of Science and Technology, Federal University of São Paulo (Unifesp), São José dos Campos, SP, Brazil;Department of Computer Science & Engineering and Center for Cognitive Science, The Ohio State University (OSU), Columbus, OH 43210, USA;Department of Computer Science, Institute of Mathematics and Computer Science, University of São Paulo (USP), São Carlos, SP, Brazil;Department of Computer Science, Institute of Mathematics and Computer Science, University of São Paulo (USP), São Carlos, SP, Brazil;The Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei, Anhui 230031, China
Venue:
Neural Networks
Year:
2011

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Abstract

Attention is a critical mechanism for visual scene analysis. By means of attention, it is possible to break down the analysis of a complex scene to the analysis of its parts through a selection process. Empirical studies demonstrate that attentional selection is conducted on visual objects as a whole. We present a neurocomputational model of object-based selection in the framework of oscillatory correlation. By segmenting an input scene and integrating the segments with their conspicuity obtained from a saliency map, the model selects salient objects rather than salient locations. The proposed system is composed of three modules: a saliency map providing saliency values of image locations, image segmentation for breaking the input scene into a set of objects, and object selection which allows one of the objects of the scene to be selected at a time. This object selection system has been applied to real gray-level and color images and the simulation results show the effectiveness of the system.