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Computer Science > Computer Vision and Pattern Recognition

arXiv:2109.12261 (cs)
[Submitted on 25 Sep 2021]

Title:An embarrassingly simple comparison of machine learning algorithms for indoor scene classification

Authors:Bhanuka Manesha Samarasekara Vitharana Gamage
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Abstract:With the emergence of autonomous indoor robots, the computer vision task of indoor scene recognition has gained the spotlight. Indoor scene recognition is a challenging problem in computer vision that relies on local and global features in a scene. This study aims to compare the performance of five machine learning algorithms on the task of indoor scene classification to identify the pros and cons of each classifier. It also provides a comparison of low latency feature extractors versus enormous feature extractors to understand the performance effects. Finally, a simple MnasNet based indoor classification system is proposed, which can achieve 72% accuracy at 23 ms latency.
Comments: 21 pages, 4 tables, 5 figures,
Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2109.12261 [cs.CV]
  (or arXiv:2109.12261v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2109.12261

Submission history

From: Bhanuka Manesha Samarasekara Vitharana Gamage [view email]
[v1] Sat, 25 Sep 2021 02:26:52 UTC (1,616 KB)
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