Abstract


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Click to download PDF version Click to download BibTeX data Clik to view abstract L. Itti, C. Koch, Feature Combination Strategies for Saliency-Based Visual Attention Systems, Journal of Electronic Imaging, Vol. 10, No. 1, pp. 161-169, Jan 2001. [1999 impact factor: 0.667] (Cited by 475)

Abstract: Bottom-up or saliency-based visual attention allows primates to detect non-specific conspicuous targets in cluttered scenes. A classical metaphor, derived from electrophysiological and psychophysical studies, describes attention as a rapidly shiftable ``spotlight''. We use a model that reproduces the attentional scanpaths of this spotlight. Simple multi-scale ``feature maps'' detect local spatial discontinuities in intensity, color, and orientation, and are combined into a unique ``master'' or ``saliency'' map. The saliency map is sequentially scanned, in order of decreasing saliency, by the focus of attention. We here study the problem of combining feature maps, from different visual modalities (such as color and orientation), into a unique saliency map. Four combination strategies are compared using three databases of natural color images: (1) Simple normalized summation, (2) linear combination with learned weights, (3) global non-linear normalization followed by summation, and (4) local non-linear competition between salient locations followed by summation. Performance was measured as the number of false detections before the most salient target was found. Strategy (1) always yielded poorest performance and (2) best performance, with a 3 to 8-fold improvement in time to find a salient target. However, (2) yielded specialized systems with poor generalization. Interestingly, strategy (4) and its simplified, computationally efficient approximation (3) yielded significantly better performance than (1), with up to 4-fold improvement, while preserving generality.

Keywords: Attention ; saliency ; target detection ; feature integration ; learning

Themes: Model of Bottom-Up Saliency-Based Visual Attention, Computational Modeling, Computer Vision

 

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