Open Access

ARTICLE

Video Summarization Approach Based on Binary Robust Invariant Scalable Keypoints and Bisecting K-Means

by Sameh Zarif^1,2,*, Eman Morad¹, Khalid Amin¹, Abdullah Alharbi³, Wail S. Elkilani⁴, Shouze Tang⁵

1 Department of Information Technology, Faculty of Computers and Information, Menoufia University, Menoufia, 32511, Egypt
2 Department of Artificial Intelligence, Faculty of Artificial Intelligence, Egyptian Russian University, Bader, 11829, Egypt
3 Department of Computer Science, Community College, King Saud University, Riyadh, 11362, Saudi Arabia
4 College of Applied Computer Science, King Saud University, Al-Muzahmiya, 19676, Saudi Arabia
5 School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

* Corresponding Authors: Sameh Zarif. Email: ,

Computers, Materials & Continua 2024, 78(3), 3565-3583. https://doi.org/10.32604/cmc.2024.046185

Received 21 September 2023; Accepted 12 January 2024; Issue published 26 March 2024

Abstract

Due to the exponential growth of video data, aided by rapid advancements in multimedia technologies. It became difficult for the user to obtain information from a large video series. The process of providing an abstract of the entire video that includes the most representative frames is known as static video summarization. This method resulted in rapid exploration, indexing, and retrieval of massive video libraries. We propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint (BRISK) and bisecting K-means clustering algorithm. The current method effectively recognizes relevant frames using BRISK by extracting keypoints and the descriptors from video sequences. The video frames’ BRISK features are clustered using a bisecting K-means, and the keyframe is determined by selecting the frame that is most near the cluster center. Without applying any clustering parameters, the appropriate clusters number is determined using the silhouette coefficient. Experiments were carried out on a publicly available open video project (OVP) dataset that contained videos of different genres. The proposed method’s effectiveness is compared to existing methods using a variety of evaluation metrics, and the proposed method achieves a trade-off between computational cost and quality.

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Keywords

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