By Topic

On the Combination of Local Texture and Global Structure for Food Classification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Zhimin Zong ; Adv. Multimedia Res. Lab., Univ. of Wollongong, Wollongong, NSW, Australia ; Duc Thanh Nguyen ; Ogunbona, P. ; Wanqing Li

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.

Published in:

Multimedia (ISM), 2010 IEEE International Symposium on

Date of Conference:

13-15 Dec. 2010