Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Combining attention and recognition for rapid scene analysis
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
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In this paper we study how the use of a novel model of bottom-up saliency (visual attention), based on local energy and color, can significantly accelerate scene recognition and, at the same time, preserve the recognition performance. To do so, we use a mobile robot-like application where scene recognition is performed through the use of SIFT features to characterize the different scenarios, and the Nearest Neighbor rule to carry out the classification. Experimental work shows that important reductions in the size of the database of prototypes can be achieved (17.6% of the original size) without significant losses in recognition performance (from 98.5% to 96.1%), thus accelerating the classification task.