Classifying for images based on the extracted probability density function and the quasi Bayesian method

Hieu Huynh- Van (), Tuan Le-Hoang () and Tai Vo- Van ()
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Hieu Huynh- Van: Ho Chi Minh City University of Technology (HCMUT)
Tuan Le-Hoang: Vietnam National University Ho Chi Minh City
Tai Vo- Van: Can Tho University

Computational Statistics, 2024, vol. 39, issue 5, No 11, 2677-2701

Abstract: Abstract This study presents a novel algorithm for image classification based on a quasi-Bayesian approach and the extraction of probability density functions (PDFs). First, representative PDFs are extracted from each image using its features. Next, a measure is developed to evaluate the similarity between the extracted PDFs. Finally, an algorithm is established for determining prior probabilities using fuzzy clustering techniques. By combining these improvements, we develop a more efficient algorithm for classifying image data. An image is assigned to a specific group if it has the highest value of prior probability and a similar level to that group. We explain the proposed algorithm step-by-step with a numerical example and clearly demonstrate its convergence. When applied to multiple image datasets, the proposed algorithm has shown stability and efficiency, outperforming many other statistical and machine learning methods. Additionally, we have developed a Matlab procedure to apply the proposed algorithm to real image datasets. These applications demonstrate the potential of research in various fields related to the digital revolution and artificial intelligence.

Keywords: Classification problem; Empirical error; Prior probability; Probability density function (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00180-023-01400-1

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