IRO Journals
Journal of Innovative Image Processing

A Novel Content-Based Video Retrieval System Using MBH and BCHOA Optimized Multiple Features

Open Access
Published: 29 April, 2026
Pages: 558-574
  • Asha D. , Asha D.
    Research Scholar, Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University Hyderabad, Telangana, India
    Research Scholar, Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University Hyderabad, Telangana, India
  • Madhavee Latha Y. Madhavee Latha Y.
    Professor, Department of Electronics and Communication Engineering, Malla Reddy Engineering College for Women, Affiliated to JNT University, Hyderabad, Telangana, India
    Professor, Department of Electronics and Communication Engineering, Malla Reddy Engineering College for Women, Affiliated to JNT University, Hyderabad, Telangana, India
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How to Cite

D., Asha, and Madhavee Latha Y. 2026. “A Novel Content-Based Video Retrieval System Using MBH and BCHOA Optimized Multiple Features”. Journal of Innovative Image Processing 8 (2): 558-74. https://doi.org/10.36548/jiip.2026.2.007.
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Keywords

Content-Based Video Retrieval (CBVR)
Video Shot Boundary Detection (VSBD)
Motion Boundary Histogram (MBH)
Feature Selection (FS)
Binary Particle Swarm Optimization (BPSO)
Binary Grey Wolf Optimization (BGWO)
Binary Whale Optimization Algorithm (BWOA)
Binary Chimp Optimization Algorithm (BCHOA)

Abstract

Usage of multimedia data is increasing drastically every day, so being able to manage and effectively retrieve information based on its content has become important.  A content-based video retrieval (CBVR) system retrieves video data based on visual content. This paper presents a CBVR system that uses visual descriptors, including Motion Boundary Histogram (MBH) temporal features, along with RGB codes and GLCM spatial features. By analysing the optical flow derivatives, MBH features are extracted from each frame to detect shot transitions and identify key frames that summarize the video content. From the keyframes, multiple features are extracted for dataset videos in the offline process and for the query video in the online process. To reduce redundancy and improve retrieval accuracy, feature selection is performed on the feature vector using various optimization algorithms, such as BPSO, BGWO, BWOA, and BCHOA. The performance of the system is tested on sample videos from the UCF50 dataset. The comparison between query and dataset videos is evaluated by computing cosine similarity. The retrieval performance and computational efficiency of the proposed CBVR framework are compared with existing methods by achieving a compression ratio of 0.9701, an F1 score of 0.859, a precision of 0.972 and a recall rate of 0.770.

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