Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Combining Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Pachinko allocation: DAG-structured mixture models of topic correlations
ICML '06 Proceedings of the 23rd international conference on Machine learning
A PAM-based ontology concept and hierarchy learning method
Journal of Information Science
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We present in this paper a part of our work in the field of image indexing and retrieval. In this work, we are using a statistical probabilistic model called Pachinko Allocation Model (PAM). Pachinko Allocation Model (PAM) is a probabilistic topic model which uses a Discrete Acyclic Graph (DAG) structure to present and learn possibly correlations of topics which were responsible of generating words in documents, like other topic models such as Latent Dirichlet Allocation (LDA), PAM was originally proposed for text processing, it can be applied for image retrieval since we can assume that image is a text and parts of image (local points, regions,…) can represent visual words like in text processing field. We propose to apply PAM on local features extracted from images using Difference of Gaussian and Salient Invariant Feature Transform (DoG/SIFT) techniques. In a second part, PAM is applying on global features (color, texture …), these features are calculated for a set of regions resulting from 4×4 division of images. The proposition is under experimental evaluation.