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MMM
2011
Springer

Correlated PLSA for Image Clustering

7 years 10 months ago
Correlated PLSA for Image Clustering
Probabilistic Latent Semantic Analysis (PLSA) has become a popular topic model for image clustering. However, the traditional PLSA method considers each image (document) independently, which would often be conflict with the real occasion. In this paper, we presents an improved PLSA model, named Correlated Probabilistic Latent Semantic Analysis (C-PLSA). Different from PLSA, the topics of the given image are modeled by the images that are related to it. In our method, each image is represented by bag-of-visual-words. With this representation, we calculate the cosine similarity between each pair of images to capture their correlations. Then we use our C-PLSA model to generate K latent topics and Expectation Maximization (EM) algorithm is utilized for parameter estimation. Based on the latent topics, image clustering is carried out according to the estimated conditional probabilities. Extensive experiments are conducted on the publicly available database. The comparison results show that ...
Peng Li, Jian Cheng, Zechao Li, Hanqing Lu
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Where MMM
Authors Peng Li, Jian Cheng, Zechao Li, Hanqing Lu
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