Improving Recognition through Object Sub-categorization

TL;DR: A method to improve the recognition rate of Bayesian classifiers by splitting the training data and using separate classifier to learn each sub-category, using probabilistic Latent Semantic Analysis (pLSA).

Abstract: 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