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  • Research Article
  • Open Access

A Multimodal Constellation Model for Object Image Classification

  • 1Email author,
  • 2,
  • 1 and
  • 1
EURASIP Journal on Image and Video Processing20102010:426781

  • Received: 8 May 2009
  • Accepted: 17 February 2010
  • Published:


We present an efficient method for object image classification. The method is an extention of the constellation model, which is a part-based model. Generally, constellation model has two weak points. (1) It is essentially a unimodal model which is unsuitable to be applied for categories with many types of appearances. (2) The probability function that represents the constellation model requires a high calculation cost. We introduced multimodalization and speed-up technique to the constellation model to overcome these weak points. The proposed model consists of multiple subordinate constellation models so that diverse types of appearances of an object category could be described by each of them, leading to the increase of description accuracy and consequently, improvement of the classification performance. In this paper, we present how to describe each type of appearance as a subordinate constellation model without any prior knowledge regarding the types of appearances, and also the implementation of the extended model's learning in realistic time. In experiments, we confirmed the effectiveness of the proposed model by comparison to methods using BoF, and also that the model learning could be realized in realistic time.


  • Computer Vision
  • Prior Knowledge
  • Probability Function
  • Classification Performance
  • Calculation Cost

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Authors’ Affiliations

Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
Faculty of Economics and Information, Gifu Shotoku Gakuen University, 1-38, Nakauzura, Gifu 500-8288, Japan


© Yasunori Kamiya et al. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.