Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories
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
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Efficient Hypothesis Generation through Sub-categorization for Multiple Object Detection
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
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We propose a method to improve the recognition rate of Bayesian classifiers by splitting the training data and using separate classifier to learn each sub-category. We use probabilistic Latent Semantic Analysis (pLSA) to split the training set automatically into sub-categories. This sub-categorization is based on the similarity of training images in terms of object's appearance or background content. In some cases, clear separation does not exist in the training set, and splitting results in worse performance. We compute the average difference between posteriors from the pLSA model, and observing this parameter, we can decide whether splitting is useful or not. This approach has been tested on eight object categories. Experimental results validate the benefit of splitting the training set.