Abstract
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.
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References
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Blei, D., Lafferty, J.: Correlated Topic Models. Advances in Neural Information Processing Systems 18 (2006)
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by Latent Semantic Analysis. Journal of the American Society of Information Science 41(6), 391–407 (1990)
Hofmann, T.: Unsupervised Learning by Probabilistic Latent Semantic Analysis. Machine Learning 42(1-2), 177–196 (2001)
Horster, E.: Topic Models for Image Retrieval on Large-Scale Databases. University of Augsburg (2009)
Jalab, H.A.: Image Retrieval System Based on Color Layout Descriptor and Gabor Filters. In: IEEE Conference on Open Systems (2011)
LaCascia, M., Sethi, S., Sclaroff, S.: Combining Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web. In: IEEE Workshop on Content-Based Access of Image and Video Libraries, vol. (6) (1998)
Li, Y., Wang, W., Gao, W.: Object Recognition Based on Dependent Pachinko Allocation Model. In: IEEE ICIP, pp. 337–340 (2007)
Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Vedaldi, A.: An implementation of SIFT detector and descriptor, http://www.vlfeat.org/~vedaldi/code/sift.html
Wang, J.: Corel Image database, http://wang.ist.psu.edu/docs/related.shtml
Wei, L., McCallum, A.: Pachinko Allocation: DAG-Structured Mixture Models of Topic Correlations. In: International Conference on Machine Learning, Pittsburg (2006)
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Boulemden, A., Tlili, Y. (2012). Image Indexing and Retrieval with Pachinko Allocation Model: Application on Local and Global Features. In: Richards, D., Kang, B.H. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2012. Lecture Notes in Computer Science(), vol 7457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32541-0_12
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DOI: https://doi.org/10.1007/978-3-642-32541-0_12
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