Name
Abstract | ||
|---|---|---|
The emerging deep learning technology is a promising means for context recognition with multimedia data. We are interested in using the deep learning with images for context recognition in smart homes. In the home context recognition, the room layout, the environment, and the contexts to be recognized are different from one household to another. Therefore, a unique recognition model is required for every different household. For this, if we take a naive approach that uses the deep learning directly, a huge amount of labeled images are required, which is practically impossible for general households. The goal of this research is to develop an image-based context recognition method that is affordable at home. In the proposed method, we exploit a cognitive API which performs general image recognition, and retrieve the information within the image as text. By using the text as features, we classify the context with ordinal supervised machine learning. Compared with the expensive approach with deep learning, the proposed method uses generic image recognition of the cognitive API, and light-weight machine learning. As a result, the context recognition customized for every household can be achieved with much less effort. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SNPD.2019.8935793 | 2019 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) |
Keywords | Field | DocType |
context recognition,image,cognitive API,machine learning | Computer science,Ordinal number,Home context,Exploit,Artificial intelligence,Deep learning,Cognition,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-7281-1652-5 | 1 | 0.43 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sinan Chen | 1 | 1 | 3.14 |
| Sachio Saiki | 2 | 55 | 24.46 |
| Masahide Nakamura | 3 | 526 | 72.51 |