TY  - JOUR
T1  - Aggregated Features Association Classifier for
Multiple Food Items Identification
AU - Khalid Abdulateef, Salwa AU - Mahmuddin, Massudi AU - Hazlyna Harun, Nor 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 8
SP  - 2200
EP  - 2206
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.2200.2206
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.2200.2206
KW  - Image food identification
KW  -extreme learning machine
KW  -feature extraction
KW  -object recognition
KW  - image analysis
AB  - Image based food identification is an emerging research topic for much industrial application. It refers
to the capability of identifying various food items based on the visual information. Unfortunately, food items
classification is highly sensitive to the accuracy of the image segmentation which is not always satisfying due
to many factors. In this study, an aggregated features association classifier is proposed to handle the resultant
problem of non-accurate image segmentation. It uses ELM for food items classification. Also, it exploits the fact
that food items are associated with others when they are placed in the plate; the accuracy of the classifier has
been improved using features association. An accuracy of 100% is obtained for input images with over or under
segmentation errors which proves the usefulness of this algorithm.
ER  -