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Towards exploiting believe function theory for object based scene classification problem

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Abstract

Scene classification is one of the active research domains of artificial intelligence (AI) with many real-world applications. This paper presents a new scene classification approach based on the Belief Function Theory, which provides a more effective way of handling uncertainty information compared to traditional probability-based methods. Unlike previous methods that rely on probabilities, which have proved their limitations, the main contribution of our approach is the use of belief degrees to classify unknown scenes based on object labels. We conduct experiments on three well-known datasets (SUN397, MIT Indoor, and LabelMe) and compare our results with state-of-the-art methods. Our approach achieves competitive results with a simple and robust framework that outperforms previous methods in some cases. We also provide insights into the strengths and limitations of our approach and discuss potential future directions for research. Overall, our work demonstrates the effectiveness of the Belief Function theory in scene classification and opens up new avenues for further research and innovation in this area.

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Data availability

The datasets generated during and/or analyzed during the current study are available in the following repositories:

MIT Indoor dataset: The MIT Indoor dataset used in this study is available in the MIT Indoor dataset repository at https://web.mit.edu/torralba/www/indoor.html.

SUN397 dataset: The SUN397 dataset used in this study is available in the SUN397 dataset repository at https://vision.princeton.edu/projects/2010/SUN/.

LabelMe dataset: The LabelMe dataset used in this study is available in the LabelMe dataset repository at http://labelme2.csail.mit.edu/Release3.0/browserTools/php/publications.php

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Acknowledgements

We would like to thank M. Mohamed Rahal and M. Mouad Oubouchou for their contribution to this work with their Bechlor thesis entitled "Contribution à l'élaboration d'une plateforme de collaboration en ligne de génération automatique de scènes" (2018)

Funding

They affirm that no financial or personal relationships could impact or bias the objectivity, interpretations, or conclusions presented in the paper. This research was conducted without any external influence or vested interests, maintaining an unbiased and impartial approach driven solely by the pursuit of scientific inquiry and the advancement of knowledge.

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Authors and Affiliations

  1. Computer Science Faculty, University of Science and Technology Houari Boumediene, Algiers, Algeria

    Anfel Amirat & Nadia Baha

  2. KU Leuven, Faculty of Arts, B-3000, Leuven, Belgium

    Lamine Benrais

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  1. Anfel Amirat
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  2. Lamine Benrais
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Correspondence to Anfel Amirat.

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Amirat, A., Benrais, L. & Baha, N. Towards exploiting believe function theory for object based scene classification problem. Multimed Tools Appl 83, 39235–39253 (2024). https://doi.org/10.1007/s11042-023-17120-z

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