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Object detection framework for multiclass food object localization and classification


Razali, Mohd Norhisham and Manshor, Noridayu (2018) Object detection framework for multiclass food object localization and classification. Advanced Science Letters, 24 (2). 1357 - 1361. ISSN 1936-6612; ESSN: 1936-7317


Detecting the instances of an object-class is a very important and crucial task in computer vision system prior to obtaining any further information. To determine the location of the object instances possess several challenges resulted from the object and image variations. In this paper, we propose a recognition framework for multiclass object detection to localize and classify the food objects to address the problem of searching multiclass objects. A typical food object, to compare to the other objects has non-rigid deformation and suffers from very large intraclass variance and too little inter-class similarities. To strive a better recognition performance while designing this framework, the optimal food recognition components comprising localization, feature extraction and classification strategy were discovered through a literature review. Besides that, the problems that are still remaining in this area critically discussed along with research direction that should be put into concentration for the future research.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.1166/asl.2018.10749
Publisher: American Scientific Publishers
Keywords: Food object detection; Image classification; Multiclass object detection; Object detection
Depositing User: Mr. Sazali Mohamad
Date Deposited: 03 Dec 2019 15:44
Last Modified: 03 Dec 2019 15:44
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1166/asl.2018.10749
URI: http://psasir.upm.edu.my/id/eprint/75111
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