A Bayesian model for efficient visual search and recognition |
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| Authors: | Lior Elazary Laurent Itti |
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| Affiliation: | a Department of Computer Science, University of Southern California, Los Angeles, CA 90089-2520, USA
b Department of Computer Science and Neuroscience Graduate Program, University of Southern California, Los Angeles, CA 90089-2520, USA |
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| Abstract: | Humans employ interacting bottom-up and top-down processes to significantly speed up search and recognition of particular targets. We describe a new model of attention guidance for efficient and scalable first-stage search and recognition with many objects (117,174 images of 1147 objects were tested, and 40 satellite images). Performance for recognition is on par or better than SIFT and HMAX, while being, respectively, 1500 and 279 times faster. The model is also used for top-down guided search, finding a desired object in a 5×5 search array within four attempts, and improving performance for finding houses in satellite images. |
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| Keywords: | Recognition Search Attention Feature Scene analysis |
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