IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
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
Pictorial Structures for Object Recognition
International Journal of Computer Vision
A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
A Comparison of Affine Region Detectors
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
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
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Discovering class-specific informative regions for a given concept with a few images is an interesting but very challenging task, due to occlusion, scale changes of objects, as well as different views under varying lighting conditions. This paper proposes a new perspective to discover the informative regions by using several images. To achieve this, we introduce a new representation of image: Ordered-BoW Image (BoWI), whose elements summarizes information of the patch centered at the element in original image. Because of its “structured pixels”, BoWI is robust and informative enough for an object class representation. Histogram-based Multi-Ranking Amalgamation Strategy (MRAS) is adopted to explore the most informative patches for an object in BoWI. Experiments on Landmark-National Icon data set that our approach is robust to occlusion, scale and illumination, and achieves promising performance in discovering class-specific informative regions.