Presentation | 2010-07-23 Classification of Food Images with the Visual Words, Constructed from Multiple Image Features Seiichi KONYA, Katsuyoshi TANABE, Tadasu UCHIYAMA, |
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Abstract(in English) | Recently, health guidance support via meal images are available and network communications about cooking has becoming popular. Extracting information from food images has been required. We propose the food image classification algorithms using visual words. Previously, visual words are constructed from single feature, such as intensity edge. In contrast, our algorithms integrate multiple features, such as average color, lightness texture and color tone, efficiently. We construct the dataset, which consists of 13 food categories, about 900 images, and evaluate our algorithms on it. In the experiments, the classification accuracies, those contain correct answers in first, second and third ranks, are 58.7, 74.4 and 83.3 percent respectively. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | image classification / local features / visual words |
Paper # | LOIS2010-16 |
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Committee | LOIS |
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Conference Date | 2010/7/15(1days) |
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Registration To | Life Intelligence and Office Information Systems (LOIS) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Classification of Food Images with the Visual Words, Constructed from Multiple Image Features |
Sub Title (in English) | |
Keyword(1) | image classification |
Keyword(2) | local features |
Keyword(3) | visual words |
1st Author's Name | Seiichi KONYA |
1st Author's Affiliation | NTT Cyber Solutions Laboratories() |
2nd Author's Name | Katsuyoshi TANABE |
2nd Author's Affiliation | NTT Cyber Solutions Laboratories |
3rd Author's Name | Tadasu UCHIYAMA |
3rd Author's Affiliation | NTT Cyber Solutions Laboratories |
Date | 2010-07-23 |
Paper # | LOIS2010-16 |
Volume (vol) | vol.110 |
Number (no) | 141 |
Page | pp.pp.- |
#Pages | 5 |
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