Fuzzy encoding with hybrid pooling for visual dictionary in food recognition
Tremendous number of f food images in the social media services can be exploited by using food recognition for healthcare benefits and food industry marketing. The main challenges in food recognition are the large variability of food appearance that often generates a highly diverse and ambiguous des...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Advanced Engineering and Science (IAES)
2021
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| Online Access: | http://psasir.upm.edu.my/id/eprint/97413/ http://psasir.upm.edu.my/id/eprint/97413/1/ABSTRACT.pdf |
| _version_ | 1848862592803536896 |
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| author | Razali, Mohd Norhisham Manshor, Noridayu Abdul Halin, Alfian Mustapha, Norwati Yaakob, Razali |
| author_facet | Razali, Mohd Norhisham Manshor, Noridayu Abdul Halin, Alfian Mustapha, Norwati Yaakob, Razali |
| author_sort | Razali, Mohd Norhisham |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Tremendous number of f food images in the social media services can be exploited by using food recognition for healthcare benefits and food industry marketing. The main challenges in food recognition are the large variability of food appearance that often generates a highly diverse and ambiguous descriptions of local feature. Ironically, the ambiguous descriptions of local feature have triggered information loss in visual dictionary constructions from the hard assignment practices. The current method based on hard assignment and Fisher vector approach to construct visual dictionary have unexpectedly cause errors from the uncertainty problem during visual word assignation. This research proposes a method of combination in soft assignment technique by using fuzzy encoding approach and maximum pooling technique to aggregate the features to produce a highly discriminative and robust visual dictionary across various local features and machine learning classifiers. The local features by using MSER detector with SURF descriptor was encoded by using fuzzy encoding approach. Support vector machine (SVM) with linear kernel was employed to evaluate the effect of fuzzy encoding. The results of the experiments have demonstrated a noteworthy classification performance of fuzzy encoding approach compared to the traditional approach based on hard assignment and Fisher vector technique. The effects of uncertainty and plausibility were minimized along with more discriminative and compact visual dictionary representation. |
| first_indexed | 2025-11-15T13:19:29Z |
| format | Article |
| id | upm-97413 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T13:19:29Z |
| publishDate | 2021 |
| publisher | Institute of Advanced Engineering and Science (IAES) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-974132022-08-26T08:47:18Z http://psasir.upm.edu.my/id/eprint/97413/ Fuzzy encoding with hybrid pooling for visual dictionary in food recognition Razali, Mohd Norhisham Manshor, Noridayu Abdul Halin, Alfian Mustapha, Norwati Yaakob, Razali Tremendous number of f food images in the social media services can be exploited by using food recognition for healthcare benefits and food industry marketing. The main challenges in food recognition are the large variability of food appearance that often generates a highly diverse and ambiguous descriptions of local feature. Ironically, the ambiguous descriptions of local feature have triggered information loss in visual dictionary constructions from the hard assignment practices. The current method based on hard assignment and Fisher vector approach to construct visual dictionary have unexpectedly cause errors from the uncertainty problem during visual word assignation. This research proposes a method of combination in soft assignment technique by using fuzzy encoding approach and maximum pooling technique to aggregate the features to produce a highly discriminative and robust visual dictionary across various local features and machine learning classifiers. The local features by using MSER detector with SURF descriptor was encoded by using fuzzy encoding approach. Support vector machine (SVM) with linear kernel was employed to evaluate the effect of fuzzy encoding. The results of the experiments have demonstrated a noteworthy classification performance of fuzzy encoding approach compared to the traditional approach based on hard assignment and Fisher vector technique. The effects of uncertainty and plausibility were minimized along with more discriminative and compact visual dictionary representation. Institute of Advanced Engineering and Science (IAES) 2021 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/97413/1/ABSTRACT.pdf Razali, Mohd Norhisham and Manshor, Noridayu and Abdul Halin, Alfian and Mustapha, Norwati and Yaakob, Razali (2021) Fuzzy encoding with hybrid pooling for visual dictionary in food recognition. Indonesian Journal of Electrical Engineering and Computer Science, 21 (1). 179 - 195. ISSN 2502-4752 https://ijeecs.iaescore.com/index.php/IJEECS/article/view/21679 10.11591/ijeecs.v21.i1.pp179-195 |
| spellingShingle | Razali, Mohd Norhisham Manshor, Noridayu Abdul Halin, Alfian Mustapha, Norwati Yaakob, Razali Fuzzy encoding with hybrid pooling for visual dictionary in food recognition |
| title | Fuzzy encoding with hybrid pooling for visual dictionary in food recognition |
| title_full | Fuzzy encoding with hybrid pooling for visual dictionary in food recognition |
| title_fullStr | Fuzzy encoding with hybrid pooling for visual dictionary in food recognition |
| title_full_unstemmed | Fuzzy encoding with hybrid pooling for visual dictionary in food recognition |
| title_short | Fuzzy encoding with hybrid pooling for visual dictionary in food recognition |
| title_sort | fuzzy encoding with hybrid pooling for visual dictionary in food recognition |
| url | http://psasir.upm.edu.my/id/eprint/97413/ http://psasir.upm.edu.my/id/eprint/97413/ http://psasir.upm.edu.my/id/eprint/97413/ http://psasir.upm.edu.my/id/eprint/97413/1/ABSTRACT.pdf |