Image-based soft drink type classification and dietary assessment system using deep convolutional neural network with transfer learning
Nowadays, people are taking soft drinks (carbonated nonalcoholic beverages) at an increasing rate. Health experts around the world have cautioned from time to time that these drinks lead to weight gain, raise the risk of non-communicable diseases, and so on. To develop consciousness among people, th...
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| Format: | Article |
| Language: | English |
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Springer
2022-05-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157820304535 |
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| author | Rubaiya Hafiz Mohammad Reduanul Haque Aniruddha Rakshit Mohammad Shorif Uddin |
| author_facet | Rubaiya Hafiz Mohammad Reduanul Haque Aniruddha Rakshit Mohammad Shorif Uddin |
| author_sort | Rubaiya Hafiz |
| collection | DOAJ |
| description | Nowadays, people are taking soft drinks (carbonated nonalcoholic beverages) at an increasing rate. Health experts around the world have cautioned from time to time that these drinks lead to weight gain, raise the risk of non-communicable diseases, and so on. To develop consciousness among people, the present work describes an image-based tool to self-monitor the nutritional information of soft drinks by using a deep convolutional neural network (CNN) along with transfer learning. At first, a pre-processing function is done through noise reduction and contrast enhancement. Then the location of the drinks region is extracted through visual saliency and mean-shift segmentation technique. After removing backgrounds and segment out only the region of interest from the image a deep CNN-based transfer learning model is employed for the drink classification. Finally, the size of each drink bottle is estimated using the bag-of-feature (BoF) and distance ratio calculation to find the nutrition value from the nutrition fact table. To perform experimentation a dataset is built containing ten most consumed soft drinks in Bangladesh using images from the ImageNet dataset, internet sources and also self-capturing. The experiment confirms that our system can detect and recognize different types of drinks with an accuracy of 98.51%. |
| format | Article |
| id | doaj-art-4ebf4e0bfba942ee96288ecf10ef5a54 |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2022-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-4ebf4e0bfba942ee96288ecf10ef5a542025-08-20T03:51:58ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-05-013451775178410.1016/j.jksuci.2020.08.015Image-based soft drink type classification and dietary assessment system using deep convolutional neural network with transfer learningRubaiya Hafiz0Mohammad Reduanul Haque1Aniruddha Rakshit2Mohammad Shorif Uddin3Dept. of Computer Science & Engineering, Daffodil International University, Dhaka, Bangladesh; Corresponding author.Dept. of Computer Science & Engineering, Daffodil International University, Dhaka, BangladeshDept. of Computer Science & Engineering, Daffodil International University, Dhaka, BangladeshDept. of Computer Science & Engineering, Jahangirnagar University, Savar, BangladeshNowadays, people are taking soft drinks (carbonated nonalcoholic beverages) at an increasing rate. Health experts around the world have cautioned from time to time that these drinks lead to weight gain, raise the risk of non-communicable diseases, and so on. To develop consciousness among people, the present work describes an image-based tool to self-monitor the nutritional information of soft drinks by using a deep convolutional neural network (CNN) along with transfer learning. At first, a pre-processing function is done through noise reduction and contrast enhancement. Then the location of the drinks region is extracted through visual saliency and mean-shift segmentation technique. After removing backgrounds and segment out only the region of interest from the image a deep CNN-based transfer learning model is employed for the drink classification. Finally, the size of each drink bottle is estimated using the bag-of-feature (BoF) and distance ratio calculation to find the nutrition value from the nutrition fact table. To perform experimentation a dataset is built containing ten most consumed soft drinks in Bangladesh using images from the ImageNet dataset, internet sources and also self-capturing. The experiment confirms that our system can detect and recognize different types of drinks with an accuracy of 98.51%.http://www.sciencedirect.com/science/article/pii/S1319157820304535Drinks classificationNoise reduction and contrast enhancementMean-shift segmentationBag-of-featureDeep CNN models |
| spellingShingle | Rubaiya Hafiz Mohammad Reduanul Haque Aniruddha Rakshit Mohammad Shorif Uddin Image-based soft drink type classification and dietary assessment system using deep convolutional neural network with transfer learning Journal of King Saud University: Computer and Information Sciences Drinks classification Noise reduction and contrast enhancement Mean-shift segmentation Bag-of-feature Deep CNN models |
| title | Image-based soft drink type classification and dietary assessment system using deep convolutional neural network with transfer learning |
| title_full | Image-based soft drink type classification and dietary assessment system using deep convolutional neural network with transfer learning |
| title_fullStr | Image-based soft drink type classification and dietary assessment system using deep convolutional neural network with transfer learning |
| title_full_unstemmed | Image-based soft drink type classification and dietary assessment system using deep convolutional neural network with transfer learning |
| title_short | Image-based soft drink type classification and dietary assessment system using deep convolutional neural network with transfer learning |
| title_sort | image based soft drink type classification and dietary assessment system using deep convolutional neural network with transfer learning |
| topic | Drinks classification Noise reduction and contrast enhancement Mean-shift segmentation Bag-of-feature Deep CNN models |
| url | http://www.sciencedirect.com/science/article/pii/S1319157820304535 |
| work_keys_str_mv | AT rubaiyahafiz imagebasedsoftdrinktypeclassificationanddietaryassessmentsystemusingdeepconvolutionalneuralnetworkwithtransferlearning AT mohammadreduanulhaque imagebasedsoftdrinktypeclassificationanddietaryassessmentsystemusingdeepconvolutionalneuralnetworkwithtransferlearning AT aniruddharakshit imagebasedsoftdrinktypeclassificationanddietaryassessmentsystemusingdeepconvolutionalneuralnetworkwithtransferlearning AT mohammadshorifuddin imagebasedsoftdrinktypeclassificationanddietaryassessmentsystemusingdeepconvolutionalneuralnetworkwithtransferlearning |