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Báo cáo sinh học: " Research Article A Multimodal Constellation Model for Object Image Classification"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article A Multimodal Constellation Model for Object Image Classification | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2010 Article ID 426781 10 pages doi 10.1155 2010 426781 Research Article A Multimodal Constellation Model for Object Image Classification Yasunori Kamiya 1 Tomokazu Takahashi 2 Ichiro Ide 1 and Hiroshi Murase1 1 Graduate School of Information Science Nagoya University Furo-cho Chikusa-ku Nagoya 464-8601 Japan 2 Faculty of Economics and Information Gifu Shotoku Gakuen University 1-38 Nakauzura Gifu 500-8288 Japan Correspondence should be addressed to Yasunori Kamiya kamiya@murase.m.is.nagoya-u.ac.jp Received 8 May 2009 Revised 19 November 2009 Accepted 17 February 2010 Academic Editor Benoit Huet Copyright 2010 Yasunori Kamiya et al. 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. 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 .