2007 Fiscal Year Final Research Report Summary
Study on the adaptive high-dimensional information retrieval based on learning and its applications
| Project/Area Number |
18500110
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Shinshu University |
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Principal Investigator |
MARUYAMA Minoru Shinshu University, Faculty of Engineering, Associate Prof. (80283232)
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| Project Period (FY) |
2006 – 2007
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| Keywords | learning / probabilistic tonic models / EM algorithm / image classification / image segmentation / image retrieval / image annotation |
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| Research Abstract |
In this research project, we investigate the content- based image analysis via learning probabilistic generative model from examples Nowadays, there exists various kinds of media information, such images, movies, on the network 'lb efficiently access to such media information, the method of content-based information retrieval is required. 'lb realize the method, in this research, we deal with the content-based image categorization, segmentation, retrieval and image annotation. For that purpose we exploit the probabilistic topic models. In the models latent topic variables am introduced to represent the content of the image. Among the probabilistic topic models, we mainly use mixture model, pLSA (probabilistic latent semantic analysis), LDA (latent Ditichlet allocation) for image analysis. For learning model parameters from examples, we use EM algorithm (for mixture model and pLSA) and variational methods (for IDA). Based on the topic models, we propose methods for image categorization based on the estimation of the conditional probability p(category I image), bottom-up image segmentation for document images, and content-based image retrieval and image annotation based on the quay words. For image annotation and image retrieval by query word, probabilistic model of image and keywords is required. We propose several models for the task. We examine the effectiveness of the proposed methods by experiments using image database, including Caltech101 database and MIT LabelMe database.
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Research Products
(4 results)
