Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
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International Journal of Computer Vision
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ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Integrating Representative and Discriminative Models for Object Category Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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Improving Recognition through Object Sub-categorization
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
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ACM Transactions on Intelligent Systems and Technology (TIST)
The 2005 PASCAL visual object classes challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
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Hypothesis generation and verification technique has recently attracted much attention in the research on multiple object category detection and localization in images. However, the performance of this strategy greatly depends on the accuracy of generated hypotheses. This paper proposes a method of multiple category object detection adopting the hypothesis generation and verification strategy that can solve the accurate hypothesis generation problem by sub-categorization. Our generative learning algorithm automatically sub-categorizes images of each category into one or more different groups depending on the object's appearance changes. Based on these sub-categories, efficient hypotheses are generated for each object category within an image in the recognition stage. These hypotheses are then verified to determine the appropriate object categories with their locations using the discriminative classifier. We compare our approach with previous related methods on various standards and the authors' own datasets. The results show that our approach outperforms the state-of-the-art methods.