On the Role of Object-Specific Features for Real World Object Recognition in Biological Vision
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Detecting image near-duplicate by stochastic attributed relational graph matching with learning
Proceedings of the 12th annual ACM international conference on Multimedia
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Near-duplicate keyframe retrieval with visual keywords and semantic context
Proceedings of the 6th ACM international conference on Image and video retrieval
Near-duplicate keyframe retrieval by nonrigid image matching
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Keyframe retrieval by keypoints: can point-to-point matching help?
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Near-Duplicate Keyframe Identification With Interest Point Matching and Pattern Learning
IEEE Transactions on Multimedia
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This paper presents a method of max-pooling spatially-coherent pyramid matching (MpScPM). Higher-layer representations are generated from lower-layer subregions, by a biologically-inspired max pooling strategy. Second, instead of reshaping the pyramid representation into a vector (used in generic SPM), the layer and location information of each subregion are kept and weak geometrical correspondences between matched subregions are explored to enhance our pyramid matching method. To enhance the possibility of finding the best matches at different scales and locations, cross-layer region similarities are computed, while the correspondences (either spatial neighbors or adjacent layers) are also incorporated. We evaluate our proposed MpScPM method on several existing benchmark datasets and it achieves excellent performances.