Classification of streaming platform images using local binary patterns and machine learning algorithms

Authors

DOI:

https://doi.org/10.31349/RevMexFis.71.051303

Keywords:

Images, algorithms, streaming platforms, machine leaarning, texture features, local binary pattern

Abstract

This research has developed an effective multi-class classification model for images from streaming platforms. Texture features were extracted from the images and used with machine learning algorithms. Two datasets were employed: ’mosaics’ comprising 153,488 images across 14 classes and ’descriptors’ containing 33,471 images across 11 classes. All images had a resolution of 1280 × 720 pixels. The local binary pattern (LBP) technique encoded the local texture structure into 59-element feature vectors for each image. Ten algorithms were trained and evaluated on these vectors for each dataset, including support vector machines (SVM) with linear, polynomial, and Gaussian kernels at various scales, as well as ensemble methods like boosted trees, bagging, discriminant analysis, and k-nearest neighbors in subspaces. Training and validation were done via 30 random splits to mitigate bias. Performance metrics like accuracy, sensitivity, specificity, precision, and F1-score were computed per class. The SVM classifier achieved top mean performance: 0.998952 accuracy, 0.992528 sensitivity, 0.999438 specificity, 0.988132 precision, and 0.990280 F1-score. The results validate the proposed LBP feature extraction and machine learning methodology for effectively classifying images across streaming platforms.

Author Biographies

J. Álvarez-Borrego, Centro de Investigación Científica y de Educación Superior de Ensenada

JOSUÉ ÁLVAREZ-BORREGO received the B.S. degree in physical oceanography from the Facultad de Ciencias Marinas from UABC, Ensenada, México, in 1981, and the M.Sc. and Ph.D. degrees in optics from CICESE, México, in 1983 and 1993, respectively. He is currently a Professor with the Applied Physics Division, Department of Optics, CICESE. His research interests include image processing with applications to biology and medicine. He is a member of the Mexican Academy of Optics, of the National Research System (SNII), the Mexican Sciences Academy, and OPTICA (former OSA).

E. Guerra-Rosas, Facultad de Ingeniería, Arquitectura y Diseño, Universidad Autónoma de Baja California

ESPERANZA GUERRA-ROSAS was born in Durango, México. She received the B.S. degree in computer systems engineering and the M.Sc. degree in electronic engineering from the Instituto Tecnológico de Durango, México, in 2004 and 2010, respectively, and the Ph.D. degree in science (physics) from the Department of Physics Research, Universidad de Sonora, in 2017. Her research interests include image processing, pattern recognition and artificial Intelligence algorithms with applications to biology and medicine. She is a member of the Mexican Academy of Optics and of the National Research System (SNII).

L. F. López-Ávila, Centro de Investigación Científica y de Educación Superior de Ensenada

LUIS FELIPE LÓPEZ-ÁVILA is a physicist and computer vision specialist with significant contributions to academia and industry. He graduated in Physics from UABC in 2015, earned a Master of Science in Optics in 2017, and completed a Ph.D. in 2021, focusing on pattern recognition and computer vision. In 2019, he was awarded the "Jorge Ojeda Castañeda" prize by the Academia Mexicana de Óptica for his groundbreaking Ph.D. research on shift, scale, and rotation invariance in pattern recognition methodologies. In 2024, he was recognized as a candidate by the Sistema Nacional de Investigadoras e Investigadores (SNII). Dr. López-Ávila has led over 10 innovation projects for global companies, addressing challenges in computer vision, cloud computing, and generative AI.

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Published

2025-09-01

How to Cite

[1]
J. Álvarez-Borrego, E. Guerra-Rosas, and L. F. López- Ávila, “Classification of streaming platform images using local binary patterns and machine learning algorithms”, Rev. Mex. Fís., vol. 71, no. 5 Sep-Oct, pp. 051303 1–, Sep. 2025.