A SYSTEMATIC VIDEO INDEXING APPROACH USING DECISION TREE

Authors

  • Madheswaran M
  • Alexander Muthurengan Murugaiyan

DOI:

https://doi.org/10.29284/ijasis.8.2.2022.18-25

Keywords:

Video indexing, decision tree, video categorization, texture features, pattern recognition, statistical features.

Abstract

A systematic categorization approach for video indexing is presented in this paper. The amount of multimedia information that can be accessed over the internet continues to expand exponentially. Due to this growth and development of multimedia on the internet, particularly videos, there has been a rise in the need for video retrieval. The goal of this work is to discover subsets of characteristics that are suitable for indexing or categorizing video content. The features chosen from the frames of the video have dominant texture characteristics. Several statistical features have been applied for better performance with decision tree classification. The investigation included 1000 videos from different video contents, such as news, cartoon, advertisements, movies, and sports categories. Results show that the overall misclassification rate percentage was below 3%. The capability of indexing the video contents indicates the real power of the proposed system, which can enhance existing indexing services, thereby enriching the tools that are available for video indexing.

 

 

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Published

2022-12-31

How to Cite

M, M., & Murugaiyan, A. M. (2022). A SYSTEMATIC VIDEO INDEXING APPROACH USING DECISION TREE. INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES, 8(2), 18–25. https://doi.org/10.29284/ijasis.8.2.2022.18-25

Issue

Vol. 8 No. 2 (2022)

Section

Articles

License

Copyright (c) 2022 M. Madheswaran & Alexander Muthurengan Murugaiyan

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.