Title: Using fuzzy similarity measure in content-based video retrieval based on image query

Authors: Fatemeh Taheri; Kambiz Rahbar

Addresses: Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran ' Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

Abstract: The primary challenge of video retrieval systems is to retrieve videos with the highest similarity to user queries. The process of feature extraction and similarity measurement plays a crucial role in the results of content-based video retrieval. This article introduces a fuzzy similarity metric for comparing and retrieving similar videos using image-queries to address the issue of uncertainty in the similarity between queries and video frames. To this end, features are extracted from both image-query and each video frame using a pre-trained VGG-16. Similarity metrics, including frequency and continuity in similar frames to the image-query, form the basis for calculating the similarity for retrieving videos. The proposed method compensates for uncertainty in image-query and dataset videos' similarity measurements, leading to improved retrieval results. The best evaluation results with the mean accuracy metric on the UCF-11 dataset for retrieving one and ten top samples are reported as 0.862 and 0.689 respectively.

Keywords: fuzzy similarity; content-based video retrieval; image query; VGG-16 neural network.

DOI: 10.1504/IJCVR.2025.144787

International Journal of Computational Vision and Robotics, 2025 Vol.15 No.2, pp.252 - 268

Received: 24 Nov 2022
Accepted: 13 Nov 2023
Published online: 03 Mar 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article