Regularized semi-supervised latent dirichlet allocation for visual concept learning
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
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In this paper, we propose Semi-Supervised pLSA(SSpLSA) for image classification. Compared with the classic non-supervised pLSA, our method overcomes the shortcoming of poor classification performance when the features of two categories are quite similar. By introducing category label information into EM algorithm, the iteration process can be directed carefully to the desired result. SS-pLSA greatly prevents the inter-impact between different categories. The experiment results show that the proposed SS-pLSA significantly improves the performance of image classification, especially when two categories’ features are similar and difficult to distinguish by classic pLSA. In contrast to these totally supervised algorithm, SS-pLSA almost has no loss in detection rate while sharply reduces the difficulty of collecting training samples. With highly flexibility, SS-pLSA enables users to explore the trade-off between labeled number and accuracy.