Work

Analysis of food images: Features and classification

Year: 2014

Type: conference-paper

Abstract: In this paper we investigate features and their combinations for food image analysis and a classification approach based on k-nearest neighbors and vocabulary trees. The system is evaluated on a food image dataset consisting of 1453 images of eating occasions in 42 food categories which were acquired by 45 participants in natural eating conditions. The same image dataset is used to test the classification system proposed in the previously reported work [1]. Experimental results indicate that using our combination of features and vocabulary trees for classification improves the food classification performance about 22% for the Top 1 classification accuracy and 10% for the Top 4 classification accuracy.

Source: Proceedings - International Conference on Image Processing

DOI: 10.1109/icip.2014.7025555

Authors: Ye He, Chang Xu, Nitin Khanna, Carol J. Boushey, Edward J. Delp

Institutions: Purdue University West Lafayette, Graphic Era University, University of Hawaii Cancer Center

Language: English

Topic: Image Retrieval and Classification Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: green

Funders: National Cancer Institute, National Institute of Diabetes and Digestive and Kidney Diseases