Paper
19 May 1999 Comparison of feature combination strategies for saliency-based visual attention systems
Author Affiliations +
Proceedings Volume 3644, Human Vision and Electronic Imaging IV; (1999) https://doi.org/10.1117/12.348467
Event: Electronic Imaging '99, 1999, San Jose, CA, United States
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'. The model described here reproduces the attentional scanpaths of this spotlight: Simple multi-scale 'feature maps' detect local spatial discontinuities in intensity, color, orientation or optical flow, 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 study the problem of combining feature maps, from different visual modalities and with unrelated dynamic ranges, 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. 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 generations. 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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Laurent Itti and Christof Koch "Comparison of feature combination strategies for saliency-based visual attention systems", Proc. SPIE 3644, Human Vision and Electronic Imaging IV, (19 May 1999); https://doi.org/10.1117/12.348467
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KEYWORDS
Visualization

Target detection

Databases

Feature extraction

Neurons

Convolution

Visual process modeling

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