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Food image classification using local appearance and global structural information

Journal Article


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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.

Publication Date


  • 2014

Citation


  • 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.

Scopus Eid


  • 2-s2.0-84901505310

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=3133&context=smhpapers

Ro Metadata Url


  • http://ro.uow.edu.au/smhpapers/2115

Number Of Pages


  • 9

Start Page


  • 242

End Page


  • 251

Volume


  • 140

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.

Publication Date


  • 2014

Citation


  • 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.

Scopus Eid


  • 2-s2.0-84901505310

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=3133&context=smhpapers

Ro Metadata Url


  • http://ro.uow.edu.au/smhpapers/2115

Number Of Pages


  • 9

Start Page


  • 242

End Page


  • 251

Volume


  • 140