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Influence of Different Activation Functions on Deep Learning Models in Indoor Scene Images Classification

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Abstract

The success of deep learning in the field of computer vision and object recognition has made significant breakthroughs, especially in improving recognition accuracy. The scene recognition algorithms have been evolved over the years because of the developments in machine learning and deep convolution neural networks (DCNNs). In this paper, the classification of indoor scenes using three deep learning models, namely, ResNet, MobileNet, and EfficientNet is attempted. The influence of activation functions on classification accuracy is being explored. Three activation functions, namely, tanh, ReLU, and sigmoid, are deployed in the work. The MIT-67 indoor dataset is split into scenes with and without people to test its effect on the accuracy of classification. The novelty of the work includes splitting the dataset, based on the spatial layout and segregating, into two groups, namely, with people and without people. Amongst the three pre-trained models, EfficientNet has given good results.

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Funding

The authors declare that there is no external funding received; however, the necessary hardware and software support is received from the research center of the institute for carrying out the research. The authors thank Visvesvaraya Technological University (VTU), Belagavi-590018, and KLE Institute of Technology, Hubballi-580027, India for providing a platform to carrying out the research work.

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Authors and Affiliations

  1. Department of CSE, K. L. E. Institute of Technology, 580030, Hubballi, Karnataka, India

    Basavaraj S. Anami

  2. Department of ECE, K. L. E. Institute of Technology, 580030, Hubballi, Karnataka, India

    Chetan V. Sagarnal

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  1. Basavaraj S. Anami
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  2. Chetan V. Sagarnal
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Correspondence to Basavaraj S. Anami or Chetan V. Sagarnal.

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COMPLIANCE WITH ETHICAL STANDARDS

This article is a completely original work of its authors; it has not been published before and will not be sent to other publications until the PRIA Editorial Board decides not to accept it for publication.

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The process of writing and the content of the article do not give grounds for raising the issue of a conflict of interest.

Additional information

Basavaraj S. Anami is working as Principal of K. L. E. Institute of Technology, Hubballi-580027, India. He is a veteran professor in computer science and has more than 40 yr of teaching experience, including 20 yr of research experience. His research area includes agriculture/horticulture image processing and natural language processing. A senior member of IEEE and CSI. He has authored three books in computer science, published by PHI, Wiely (India) and UP.

Chetan V. Sagarnal received a Bachelor of Engineering and Master of Technology in Electronics and Communication Engineering from Visveswaraiah Technological University (VTU), Belagavi-590018, India. He has 7 years of teaching experience and pursuing his PhD in Computer Science and Engineering from VTU. His current research interests include computer vision and deep learning.

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Anami, B.S., Sagarnal, C.V. Influence of Different Activation Functions on Deep Learning Models in Indoor Scene Images Classification. Pattern Recognit. Image Anal. 32, 78–88 (2022). https://doi.org/10.1134/S1054661821040039

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