Unsupervised Image Categorization Using Constrained Entropy-Regularized Likelihood Learning with Pairwise Constraints

Authors:
Zhiwu Lu;Xiaoqing Lu;Zhiyuan Ye
Affiliations:
Institute of Computer Science and Technology, Peking University, Beijing 100871, China;Institute of Computer Science and Technology, Peking University, Beijing 100871, China;Institute of Computer Science and Technology, Peking University, Beijing 100871, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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Abstract

We usually identify the categories in image databases using some clustering algorithms based on the visual features extracted from images. Due to the well-known gap between the semantic features (e.g., categories) and the visual features, the results of unsupervised image categorization may be quite disappointing. Of course, it can be improved by adding some extra semantic information. Pairwise constraints between some images are easy to provide, even when we have little prior knowledge about the image categories in a database. A semi-supervised learning algorithm is then proposed for unsupervised image categorization based on Gaussian mixture model through incorporating such semantic information into the entropy-regularized likelihood (ERL) learning, which can automatically detect the number of image categories in the database. The experiments further show that this algorithm can lead to some promising results when applied to image categorization.