Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency, Scale and Image Description
International Journal of Computer Vision
Geometric Hashing: An Overview
IEEE Computational Science & Engineering
Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?"
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Learning a Sparse Representation for Object Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Class-Specific, Top-Down Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Recognizing Surfaces Using Three-Dimensional Textons
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Selection of Scale-Invariant Parts for Object Class Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
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In an object recognition task where an image is represented as a constellation of image patches, often many patches correspond to the cluttered background. If such patches are used for object class recognition, they will adversely affect the recognition rate. In this paper, we present a statistical method for selecting the image patches which characterize the target object class and are capable of discriminating between the positive images containing the target objects and the complementary negative images. This statistical method select those images patches from the positive images which, when used individually, have the power of discriminating between the positive and negative images in the evaluation data. Another contribution of this paper is the part-based probabilistic method for object recognition. This Bayesian approach uses a common reference frame instead of reference patch to avoid the possible occlusion problem. We also explore different feature representation using PCA an 2D PCA. The experiment demonstrates our approach has outperformed most of the other known methods on a popular benchmark data set while approaching the best known results.