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Food image classification using local appearance and global structural information

journal contribution
posted on 2014-09-22, 00:00 authored by Duc Thanh NguyenDuc Thanh Nguyen, Zhimin Zong, Philip O Ogunbona, Yasmine Probst, Wanqing Li
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.

History

Journal

Neurocomputing

Volume

140

Pagination

242 - 251

Publisher

Elsevier

Location

Amsterdam, The Netherlands

ISSN

0925-2312

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2014, Elsevier B.V.

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