A graph distance metric based on the maximal common subgraph
Pattern Recognition Letters
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Selection of Scale-Invariant Parts for Object Class Recognition
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
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Semi-supervised learning with graphs
Semi-supervised learning with graphs
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Object Recognition as Many-to-Many Feature Matching
International Journal of Computer Vision
Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
A generative model for graph matching and embedding
Computer Vision and Image Understanding
Graph characteristics from the heat kernel trace
Pattern Recognition
Foreground Focus: Unsupervised Learning from Partially Matching Images
International Journal of Computer Vision
Graph-Based Object Class Discovery
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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In this paper, we present a new method to improve the performance of current bag-of-word based image classification process. After feature extraction, we introduce a pairwise image matching scheme to select the discriminative features. Only the labeled information from the training-sets is used to update the feature weights via an iterative matching processing. The selected features correspond to the foreground content of the images thus highlight the high level category knowledge of images. ''Visual words'' are constructed on these selected features. Our method can be used as a refinement step for current image classification and retrieval process. We prove the efficiency of our method in three tasks: supervised image classification, semi-supervised image classification and image retrieval. In the experimental part, two canonical datasets Caltech 256 and MSRC-v2 are used. Our methods have increased the performance of given image analysis tasks. The accuracies of supervised and semi-supervised image classification has been increased up to 21%. Meanwhile, the precision of image retrieval results has also been improved by using our method.