A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
PicSOM—content-based image retrieval with self-organizing maps
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Emergent Semantics through Interaction in Image Databases
IEEE Transactions on Knowledge and Data Engineering
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Spatial Weighting for Bag-of-Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Computational visual attention systems and their cognitive foundations: A survey
ACM Transactions on Applied Perception (TAP)
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
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The recognition of object categories is one of the most challenging problems in computer vision field. It is still an open problem , especially in content based image retrieval (CBIR).When using analysis algorithm, a trade-off must be found between the quality of the results expected, and the amount of computer resources allocated to manage huge amount of generated data. In human, the mechanisms of evolution have generated the visual attention system which selects the most important information in order to reduce both cognitive load and scene understanding ambiguity. In computer science, most powerful algorithms use local approaches as bag-of-features or sparse local features. In this article, we propose to evaluate the integration of one of the most recent visual attention model in one of the most efficient CBIR method. First, we present these two algorithms and the database used to test results. Then, we present our approach which consists in pruning interest points in order to select a certain percentage of them (40% to 10%). This filtering is guided by a saliency map provided by a visual attention system. Finally, we present our results which clearly demonstrate that interest points used in classical CBIR methods can be drastically pruned without seriously impacting results. We also demonstrate that we have to smartly filter learning and training data set to obtain such results.