Toward Flexible and Efficient Home Context Sensing: Capability Evaluation and Verification of Image-Based Cognitive APIs
Cognitive Application Program Interface (API) is an API of emerging artificial intelligence (AI)-based cloud services, which extracts various contextual information from non-numerical multimedia data including image and audio. Our interest is to apply image-based cognitive APIs to implement flexible...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/5/1442 |
| _version_ | 1811307851631558656 |
|---|---|
| author | Sinan Chen Sachio Saiki Masahide Nakamura |
| author_facet | Sinan Chen Sachio Saiki Masahide Nakamura |
| author_sort | Sinan Chen |
| collection | DOAJ |
| description | Cognitive Application Program Interface (API) is an API of emerging artificial intelligence (AI)-based cloud services, which extracts various contextual information from non-numerical multimedia data including image and audio. Our interest is to apply image-based cognitive APIs to implement flexible and efficient context sensing services in a smart home. In the existing approach with machine learning by us, with the complexity of recognition object and the number of the defined contexts increases by users, it still requires directly manually labeling a moderate scale of data for training and continually try to calling multiple cognitive APIs for feature extraction. In this paper, we propose a novel method that uses a small scale of labeled data to evaluate the capability of cognitive APIs in advance, before training features of the APIs with machine learning, for the flexible and efficient home context sensing. In the proposed method, we exploit document similarity measures and the concepts (i.e., internal cohesion and external isolation) integrate into clustering results, to see how the capability of different cognitive APIs for recognizing each context. By selecting the cognitive APIs that relatively adapt to the defined contexts and data based on the evaluation results, we have achieved the flexible integration and efficient process of cognitive APIs for home context sensing. |
| first_indexed | 2024-04-13T09:11:43Z |
| format | Article |
| id | doaj.art-e23fdded87434fd98d06fa9593246faf |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T09:11:43Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e23fdded87434fd98d06fa9593246faf2022-12-22T02:52:52ZengMDPI AGSensors1424-82202020-03-01205144210.3390/s20051442s20051442Toward Flexible and Efficient Home Context Sensing: Capability Evaluation and Verification of Image-Based Cognitive APIsSinan Chen0Sachio Saiki1Masahide Nakamura2Graduate School of System Informatics, Kobe University, 1-1 Rokkodai-cho, Nada, Kobe 657-8501, JapanGraduate School of System Informatics, Kobe University, 1-1 Rokkodai-cho, Nada, Kobe 657-8501, JapanGraduate School of System Informatics, Kobe University, 1-1 Rokkodai-cho, Nada, Kobe 657-8501, JapanCognitive Application Program Interface (API) is an API of emerging artificial intelligence (AI)-based cloud services, which extracts various contextual information from non-numerical multimedia data including image and audio. Our interest is to apply image-based cognitive APIs to implement flexible and efficient context sensing services in a smart home. In the existing approach with machine learning by us, with the complexity of recognition object and the number of the defined contexts increases by users, it still requires directly manually labeling a moderate scale of data for training and continually try to calling multiple cognitive APIs for feature extraction. In this paper, we propose a novel method that uses a small scale of labeled data to evaluate the capability of cognitive APIs in advance, before training features of the APIs with machine learning, for the flexible and efficient home context sensing. In the proposed method, we exploit document similarity measures and the concepts (i.e., internal cohesion and external isolation) integrate into clustering results, to see how the capability of different cognitive APIs for recognizing each context. By selecting the cognitive APIs that relatively adapt to the defined contexts and data based on the evaluation results, we have achieved the flexible integration and efficient process of cognitive APIs for home context sensing.https://www.mdpi.com/1424-8220/20/5/1442smart homecontextscognitive apiimagedocument similarityinternal cohesionexternal isolationclustering |
| spellingShingle | Sinan Chen Sachio Saiki Masahide Nakamura Toward Flexible and Efficient Home Context Sensing: Capability Evaluation and Verification of Image-Based Cognitive APIs Sensors smart home contexts cognitive api image document similarity internal cohesion external isolation clustering |
| title | Toward Flexible and Efficient Home Context Sensing: Capability Evaluation and Verification of Image-Based Cognitive APIs |
| title_full | Toward Flexible and Efficient Home Context Sensing: Capability Evaluation and Verification of Image-Based Cognitive APIs |
| title_fullStr | Toward Flexible and Efficient Home Context Sensing: Capability Evaluation and Verification of Image-Based Cognitive APIs |
| title_full_unstemmed | Toward Flexible and Efficient Home Context Sensing: Capability Evaluation and Verification of Image-Based Cognitive APIs |
| title_short | Toward Flexible and Efficient Home Context Sensing: Capability Evaluation and Verification of Image-Based Cognitive APIs |
| title_sort | toward flexible and efficient home context sensing capability evaluation and verification of image based cognitive apis |
| topic | smart home contexts cognitive api image document similarity internal cohesion external isolation clustering |
| url | https://www.mdpi.com/1424-8220/20/5/1442 |
| work_keys_str_mv | AT sinanchen towardflexibleandefficienthomecontextsensingcapabilityevaluationandverificationofimagebasedcognitiveapis AT sachiosaiki towardflexibleandefficienthomecontextsensingcapabilityevaluationandverificationofimagebasedcognitiveapis AT masahidenakamura towardflexibleandefficienthomecontextsensingcapabilityevaluationandverificationofimagebasedcognitiveapis |