Food image classification using local appearance and global structural information

Authors

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

RIS ID

89631

Publication Details

Nguyen, D., Zong, z., Ogunbona, P. O., Probst, Y. & Li, W. (2014). Food image classification using local appearance and global structural information. Neurocomputing, 140 242-251.

Abstract

This paper proposes food image classification methods exploiting both local appearance and global structural information of food objects. The contribution of the paper is threefold. First, non-redundant local binary pattern (NRLBP) is used to describe the local appearance information of food objects. Second, the structural information of food objects is represented by the spatial relationship between interest points and encoded using a shape context descriptor formed from those interest points. Third, we propose two methods of integrating appearance and structural information for the description and classification of food images. We evaluated the proposed methods on two datasets. Experimental results verified that the combination of local appearance and structural features can improve classification performance.