Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
PLSA-based image auto-annotation: constraining the latent space
Proceedings of the 12th annual ACM international conference on Multimedia
Matching Theory (North-Holland mathematics studies)
Matching Theory (North-Holland mathematics studies)
A latent topic model for linked documents
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Semantic concept annotation based on audio PLSA model
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Multilayer pLSA for multimodal image retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Image categorization via robust pLSA
Pattern Recognition Letters
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Effective Image Retrieval Based on Hidden Concept Discovery in Image Database
IEEE Transactions on Image Processing
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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 our approach is superior to the traditional PLSA for image clustering.