A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contrast-based image attention analysis by using fuzzy growing
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
A Coherent Computational Approach to Model Bottom-Up Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
Is bottom-up attention useful for object recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Region-of-importance detection based on fusion of audio and video
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
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In this paper we present an algorithm which uses adaptive selection of low-level features for main subject detection. The algorithm first computes low-level features such as contrast and sharpness, each computed in a block-based fashion. Next, the algorithm quantifies the usefulness of each feature by using both statistical and geometric information measured across blocks. Finally, the saliency of each block is determined via a weighted linear combination of the features, where the weights are chosen based on each feature's estimated usefulness. Our results demonstrate that the adaptive nature of this algorithm allows it to perform competitively with other techniques, while maintaining very low computational complexity.