{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T11:42:28Z","timestamp":1781610148996,"version":"3.54.5"},"reference-count":70,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,1,25]],"date-time":"2020-01-25T00:00:00Z","timestamp":1579910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To implement fine-grained context recognition that is accurate and affordable for general households, we present a novel technique that integrates multiple image-based cognitive APIs and light-weight machine learning. Our key idea is to regard every image as a document by exploiting \u201ctags\u201d derived by multiple APIs. The aim of this paper is to compare API-based models\u2019 performance and improve the recognition accuracy by preserving the affordability for general households. We present a novel method for further improving the recognition accuracy based on multiple cognitive APIs and four modules, fork integration, majority voting, score voting, and range voting.<\/jats:p>","DOI":"10.3390\/s20030666","type":"journal-article","created":{"date-parts":[[2020,1,27]],"date-time":"2020-01-27T07:41:11Z","timestamp":1580110871000},"page":"666","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Integrating Multiple Models Using Image-as-Documents Approach for Recognizing Fine-Grained Home Contexts"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9898-7370","authenticated-orcid":false,"given":"Sinan","family":"Chen","sequence":"first","affiliation":[{"name":"Graduate School of System Informatics, Kobe University, 1-1 Rokkodai-cho, Nada, Kobe 657-8501, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sachio","family":"Saiki","sequence":"additional","affiliation":[{"name":"Graduate School of System Informatics, Kobe University, 1-1 Rokkodai-cho, Nada, Kobe 657-8501, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masahide","family":"Nakamura","sequence":"additional","affiliation":[{"name":"Graduate School of System Informatics, Kobe University, 1-1 Rokkodai-cho, Nada, Kobe 657-8501, Japan"},{"name":"RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,25]]},"reference":[{"key":"ref_1","unstructured":"Vuegen, L., Van Den Broeck, B., Karsmakers, P., Van hamme, H., and Vanrumste, B. 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