On the combination of local texture and global structure for food classification

Authors

Zhimin Zong, University of Wollongong
Duc Thanh Nguyen, University of WollongongFollow
Philip O. Ogunbona, University of WollongongFollow
Wanqing Li, University of WollongongFollow

RIS ID

37310

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.