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Object recognition using saliency guided searching
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AI
DOI：10.3233/ICA-160528.png)
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Abstract
En 中文
The search for an object in an image commonly occurs by detecting interest points in the whole scene, followed by matching all descriptors extracted from the selected points with the descriptors of the target. This paper proposes an iterative analysis of the scene by using saliency maps to guide the search to more promising regions, instead of processing the whole image at once. To do so, a patch-based version of SURF (Speeded-Up Robust Features detector) is introduced just as a saliency detection method, named Background Laplacian Saliency (BLS), which is based on a pixel's distance from the estimated background of the scene combined with a Laplacian-based operator. Using BLS and several state-of-the art saliency algorithms, the application of saliency maps to guide the recognition of objects instances using the patch-based SURF is investigated. Experiments showed that when the search object is present in the scene, the search's speed is significantly improved, but, on the other hand, when the target is absent, the required processing time increases. Based on this finding, this work also proposes the application of saliency maps to define the level of detail in which each scene location should be processed. Using a novel and practical evaluation, which considers both speed and accuracy of saliency detection algorithms, it is demonstrated that the processing time of visual search is reduced to about 52% of the time required by SURF when the target's presence is guaranteed, and from 63% to 79% of that time when no guarantee exists, without any significant loss of accuracy, for both cases.
Keywords:
Saliency detection
visual search
visual attention
object recognition
feature detection
feature description and matching
Journal
I
IF:
5.3
Papers:
484
Citations:
735