| Presentation | 2019-05-31 Recognizing Fine-Grained Contexts at Home Using Multiple Cognitive APIs Sinan Chen, Sachio Saiki, Masahide Nakamura, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Recognizing fine-grained contexts in individual houses and residents has been an important but challenging research topic. It has been studied for many years in the field of ubiquitous computing. We are studying fine-grained context recognition affordable for general households, by integrating general-purpose image-based cognitive APIs and light-weight machine learning. In our previous study, we first evaluated the recognition performance of commercial APIs, and then developed a recognition method that uses the recognition results of the single API as feature values. However, we found that the method could not distinguish different contexts with multiple people such as ``Eating'' and ``Playing games'' and ``General meeting''. The goal of this research is to improve the recognition accuracy of such difficult contexts, with preserving the affordability to general households. In the proposed method, we use multiple image-based cognitive APIs. For each API, we construct an independent recognition model using feature values of the API. Then, the context is determined by majority voting among results of the independent models. Compared to the popular approach with deep learning, the proposed method does not require a huge amount of labeled data or vast computing resources. As a result, it can recognize home contexts more accurately and economically. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Home context / Image recognition / Cognitive API / Machine learning / Majority voting |
| Paper # | SC2019-6 |
| Date of Issue | 2019-05-24 (SC) |
| Conference Information | |
| Committee | SC |
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| Conference Date | 2019/5/31(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | National Institute for Materials Science |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Science Service Platform, Data Service and Machine Learning, etc |
| Chair | Masahide Nakamura(Kobe Univ.) |
| Vice Chair | Shinji Kikuchi(National Institute for Materials Science) / Yoji Yamato(NTT) |
| Secretary | Shinji Kikuchi(Tokyo University of Technology) / Yoji Yamato(Fujitsu Lab.) |
| Assistant | |
| Paper Information | |
| Registration To | Technical Committee on Service Computing |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Recognizing Fine-Grained Contexts at Home Using Multiple Cognitive APIs |
| Sub Title (in English) | Majority Voting Approach |
| Keyword(1) | Home context |
| Keyword(2) | Image recognition |
| Keyword(3) | Cognitive API |
| Keyword(4) | Machine learning |
| Keyword(5) | Majority voting |
| 1st Author's Name | Sinan Chen |
| 1st Author's Affiliation | Kobe University(Kobe Univ.) |
| 2nd Author's Name | Sachio Saiki |
| 2nd Author's Affiliation | Kobe University(Kobe Univ.) |
| 3rd Author's Name | Masahide Nakamura |
| 3rd Author's Affiliation | Kobe University(Kobe Univ.) |
| Date | 2019-05-31 |
| Paper # | SC2019-6 |
| Volume (vol) | vol.119 |
| Number (no) | SC-66 |
| Page | pp.pp.33-38(SC), |
| #Pages | 6 |
| Date of Issue | 2019-05-24 (SC) |