Article
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Indoor Scene Recognition via Object Detection and TF-IDF
Version 1
: Received: 5 July 2022 / Approved: 5 July 2022 / Online: 5 July 2022 (08:38:17 CEST)
A peer-reviewed article of this Preprint also exists.
Heikel, E.; Espinosa-Leal, L. Indoor Scene Recognition via Object Detection and TF-IDF. J. Imaging 2022, 8, 209. Heikel, E.; Espinosa-Leal, L. Indoor Scene Recognition via Object Detection and TF-IDF. J. Imaging 2022, 8, 209.
Abstract
Indoor scene recognition and semantic information can be helpful for social robots. Recently, in the field of indoor scene recognition, researchers have incorporated object-level information and shown improved performances. This paper demonstrates that scene recognition can be performed solely using object-level information in line with these advances. A state-of-the-art object detection model was trained to detect objects typically found in indoor environments and then used to detect objects in scene data. These predicted objects were then used as features to predict room categories. This paper successfully combines approaches conventionally used in computer vision (YOLO) and Term Frequency-Inverse Document Frequency (TF-IDF). These approaches could be further helpful in the field of embodied research and dynamic scene classification, which we elaborate on.
Keywords
scene recognition; object detection; scene classification; TF-IDF
Subject
Computer Science and Mathematics, Computer Science
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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