ABSTRACT
This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes on contributors
S. Jasmine Minija received her M.Phil (Computer Science) degree in 2014 from Mother Teresa Women’s University, Kodaikanal and M.Sc degree in 2013 from Manonmaniam Sundaranar University, Tirunelveli. At present she is pursuing PhD (Reg.No.12487) in the Department of Computer Science, Nesamony Memorial Christian College, Marthandam, affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, TamilNadu, India. Her research interests include Image Processing, Neural Network, and Fuzzy Logic.
W. R. Sam Emmanuel received his doctoral degree in computer science in 2012 from Vinayaka Missions University, Salem. He received his M.Phil. (Computer Science) degree in 2002 from Manonmaniam Sundaranar University, Tirunelveli, and MCA from Bharathidhason University, Salem. He is working as Associate Professor at Nesamony Memorial Christian College, Marthandam. His major research interests are Image Processing, Cryptography, Network Security, Segmentation and Classification.