Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
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
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
International Journal of Computer Vision
Improving Bag-of-Features for Large Scale Image Search
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving the fisher kernel for large-scale image classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Towards optimal naive bayes nearest neighbor
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Image classification using super-vector coding of local image descriptors
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Asymmetric hamming embedding: taking the best of our bits for large scale image search
MM '11 Proceedings of the 19th ACM international conference on Multimedia
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
A graph-matching kernel for object categorization
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Large-scale Structure-from-Motion Reconstruction with small memory consumption
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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In this paper, we propose a novel image classification framework based on patch matching. More precisely, we adapt the Hamming Embedding technique, first introduced for image search to improve the bag-of-words representation. This matching technique allows the fast comparison of descriptors based on their binary signatures, which refines the matching rule based on visual words and thereby limits the quantization error. Then, in order to allow the use of efficient and suitable linear kernel-based SVM classification, we propose a mapping method to cast the scores output by the Hamming Embedding matching technique into a proper similarity space. Comparative experiments of our proposed approach and other existing encoding methods on two challenging datasets PASCAL VOC 2007 and Caltech-256, report the interest of the proposed scheme, which outperforms all methods based on patch matching and even provide competitive results compared with the state-of-the-art coding techniques.