Open AccessA Comparative Study of Indian Food Image Classification Using K-Nearest-Neighbour and Support-Vector-Machines
Author(s) -
C Pathanjali,
Vimuktha Evangeleen Salis,
G Jalaja,
A. Gauthami Latha
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.12.16171
Subject(s) - support vector machine , artificial intelligence , pattern recognition (psychology) , classifier (uml) , computer science , nearest neighbour , k nearest neighbors algorithm , contextual image classification , machine learning , computer vision , image (mathematics)
Food being the vital part of everyone’s lives, food detection and recognition becomes an interesting and challenging problem in computer vision and image processing. In this paper we mainly propose an automatic food detection system that detects and recognises varieties of Indian food. This paper uses a combined colour and shape features. The K-Nearest-Neighbour (KNN) and Support-Vector -Machine (SVM) classification models are used to classify the features. A comparative study on the performance of both the classification models is performed. The experimental result shows the higher efficiency of SVM classifier over KNN classifier.