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On the combination of local texture and global structure for food classification
Faculty of Informatics - Papers (Archive)
  • Zhimin Zong, University of Wollongong
  • Duc Thanh Nguyen, University of Wollongong
  • Philip O Ogunbona, University of Wollongong
  • Wanqing Li, University of Wollongong
RIS ID
37310
Publication Date
1-1-2010
Publication Details

Zong, z., Nguyen, D., Ogunbona, P. & Li, W. (2010). On the combination of local texture and global structure for food classification. IEEE International Symposium on Multimedia, ISM 2010 (pp. 204-211). Piscataway, New Jersey, USA: IEEE.

Abstract

This paper proposes a food image classification method using local textural patterns and their global structure to describe the food image. In this paper, a visual codebook of local textural patterns is created by employing Scale Invariant Feature Transformation (SIFT) interest point detector with the Local Binary Pattern (LBP) feature. In addition to describing the food image using local texture, the global structure of the food object is represented as the spatial distribution of the local textural structures and encoded using shape context. We evaluated the proposed method on the Pittsburgh Fast-Food Image (PFI) dataset. Experimental results showed that the proposed method could obtain better performance than the baseline experiment on the PFI dataset.

Citation Information
Zhimin Zong, Duc Thanh Nguyen, Philip O Ogunbona and Wanqing Li. "On the combination of local texture and global structure for food classification" (2010) p. 204 - 211
Available at: http://works.bepress.com/p_ogunbona/15/