Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
International Journal of Computer Vision
Visual language modeling for image classification
Proceedings of the international workshop on Workshop on multimedia information retrieval
Learning distance functions for exemplar-based object recognition
Learning distance functions for exemplar-based object recognition
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In this paper, a novel method for object categorization is proposed. We first analyze the phenomenon of large intra-class variation and attribute it to the "subcategory" problem. To reveal the local and distinct properties of the different subcategories, relative spaces are constructed. Then the weighted FLDs (Fisher Linear Discriminant) as weak learners trained in relative spaces are integrated with the boosting framework to form the final classifier. Experiments on 8 categories from Caltech database show the effectiveness of our algorithm.