{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T17:21:52Z","timestamp":1790702512770,"version":"4.1.0"},"reference-count":38,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"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>Indoor scene recognition is a computer vision task that identifies various indoor environments, such as offices, libraries, kitchens, and restaurants. This research area is particularly significant for applications in robotics, security, and assistance for individuals with disabilities, as it enables the categorization of spaces and the provision of contextual information. Convolutional Neural Networks (CNNs) are commonly employed in this field. While CNNs perform well in outdoor scene recognition by focusing on global features such as mountains and skies, they often struggle with indoor scenes, where local features like furniture and objects are more critical. In this study, the \u201cMIT 67 Indoor Scene\u201d dataset is used to extract and combine features from both a CNN and a text-based model utilizing object recognition outputs, resulting in a two-channel hybrid model. The experimental results demonstrate that this hybrid approach, which integrates natural language processing and image processing techniques, improves the test accuracy of the image processing model by 8.3%, achieving a notable success rate. Furthermore, this study offers contributions to new application areas in remote sensing, particularly in indoor scene understanding and indoor mapping.<\/jats:p>","DOI":"10.3390\/s25175350","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T08:01:01Z","timestamp":1756454461000},"page":"5350","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Comprehensive Hybrid Approach for Indoor Scene Recognition Combining CNNs and Text-Based Features"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5385-6775","authenticated-orcid":false,"given":"Taner","family":"Uckan","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Van Yuzuncu Y\u0131l University, Van 65080, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1745-4562","authenticated-orcid":false,"given":"Cengiz","family":"Aslan","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Robotics, Van Yuzuncu Y\u0131l University, Van 65080, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5190-3504","authenticated-orcid":false,"given":"Cengiz","family":"Hark","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Inonu University, Malatya 44050, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107205","DOI":"10.1016\/j.patcog.2020.107205","article-title":"Scene Recognition: A Comprehensive Survey","volume":"102","author":"Xie","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Surendran, R., Chihi, I., Anitha, J., and Hemanth, D.J. 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