• Search Research Projects
  • Search Researchers
  • How to Use
  1. Back to previous page

Study on the adaptive high-dimensional information retrieval based on learning and its applications

Research Project

Project/Area Number 18500110
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeSingle-year Grants
Section一般
Research Field Intelligent informatics
Research InstitutionShinshu University

Principal Investigator

MARUYAMA Minoru  Shinshu University, Faculty of Engineering, Associate Prof. (80283232)

Project Period (FY) 2006 – 2007
Project Status Completed (Fiscal Year 2007)
Budget Amount *help
¥2,860,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥360,000)
Fiscal Year 2007: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2006: ¥1,300,000 (Direct Cost: ¥1,300,000)
Keywordslearning / probabilistic tonic models / EM algorithm / image classification / image segmentation / image retrieval / image annotation / pLSA / LDA / パターン識別 / SIFT / クラスタリング / 確率モデル / SVM / 混合正規分布モデル
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.

Report

(3 results)
  • 2007 Annual Research Report   Final Research Report Summary
  • 2006 Annual Research Report
  • Research Products

    (5 results)

All 2008 2007

All Journal Article (3 results) (of which Peer Reviewed: 1 results) Presentation (2 results)

  • [Journal Article] An online handwritten music symbol recognition system2007

    • Author(s)
      H. Miyao and M. Maruyama
    • Journal Title

      International Journal on Document Analysis and Recognition 9

      Pages: 49-58

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      2007 Final Research Report Summary
    • Peer Reviewed
  • [Journal Article] An online handwritten music symbol recognition system2007

    • Author(s)
      Hidetoshi, Miyao, Minoru, Maruyama
    • Journal Title

      International Journal on Document Analysis and Recognition Vol.9, No.1

      Pages: 49-58

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      2007 Final Research Report Summary
  • [Journal Article] An online music symbol recognition system2007

    • Author(s)
      Hidetoshi Miyao, Minoru Maruyama
    • Journal Title

      International Journal on Document Analysis and Recognition 9・1

      Pages: 49-58

    • Related Report
      2006 Annual Research Report
  • [Presentation] Feature extraction for document image segmentation by pLSA model2008

    • Author(s)
      T. Yamaguchi and M. Maruyama
    • Organizer
      Document Analysis Systems(DAS2008)
    • Place of Presentation
      奈良
    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      2007 Final Research Report Summary
  • [Presentation] Feature extraction for document image segmentation by pLSA model2008

    • Author(s)
      Takuma, Yamaguchi, Minoru, Maruyama
    • Organizer
      Document Analysis Systems 2008
    • Place of Presentation
      Nara
    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      2007 Final Research Report Summary

URL: 

Published: 2006-04-01   Modified: 2016-04-21  

Information User Guide FAQ News Terms of Use Attribution of KAKENHI

Powered by NII kakenhi