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Food category recognition using SURF and MSER local feature representation


Razali, Mohd Norhisham and Manshor, Noridayu and Abdul Halin, Alfian and Yaakob, Razali and Mustapha, Norwati (2017) Food category recognition using SURF and MSER local feature representation. In: Advances in Visual Informatics. Springer, Switzerland, 212 - 223. ISBN 9783319700090; EISBN: 9783319700106


Food object recognition has gained popularity in recent years. This can perhaps be attributed to its potential applications in fields such as nutrition and fitness. Recognizing food images however is a challenging task since various foods come in many shapes and sizes. Besides having unexpected deformities and texture, food images are also captured in differing lighting conditions and camera viewpoints. From a computer vision perspective, using global image features to train a supervised classifier might be unsuitable due to the complex nature of the food images. Local features on the other hand seem the better alternative since they are able to capture minute intricacies such as interest points and other intricate information. In this paper, two local features namely SURF (Speeded- Up Robust Feature) and MSER (Maximally Stable Extremal Regions) are investigated for food object recognition. Both features are computationally inexpensive and have shown to be effective local descriptors for complex images. Specifically, each feature is firstly evaluated separately. This is followed by feature fusion to observe whether a combined representation could better represent food images. Experimental evaluations using a Support Vector Machine classifier shows that feature fusion generates better recognition accuracy at 86.6%.

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Additional Metadata

Item Type: Book Section
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.1007/978-3-319-70010-6_20
Publisher: Springer
Keywords: Food category recognition; MSER; SURF; Bag of features
Depositing User: Azhar Abdul Rahman
Date Deposited: 02 Dec 2019 15:19
Last Modified: 02 Dec 2019 15:19
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1007/978-3-319-70010-6_20
URI: http://psasir.upm.edu.my/id/eprint/63113
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