Title: Colour image cross-modal retrieval method based on multi-modal visual data fusion

Authors: Xiangyuan Liu

Addresses: College of Culture Communication and Art Design, Hunan College of Information, Chang Sha, 410000, China

Abstract: Because the traditional colour image cross-modal retrieval methods have the problems of low retrieval accuracy and recall, and long retrieval time, a colour image cross-modal retrieval method based on multi-modal visual data fusion is proposed. First, multimodal visual colour images are collected, and then bilateral filtering method is used to filter the collected images to enhance colour images. Then, the enhanced multimodal visual data is coded, the image modal features and colour modal features through cross-layer fusion encoder are decomposed, and finally the decoded two modal features are fused. According to the multimodal visual data fusion results, cross-modal retrieval of colour images is performed by using two scale similarity measurement. The simulation results show that the proposed method has higher precision and recall rate for colour image cross-modal retrieval, and shorter retrieval time.

Keywords: multimodal visual data fusion; colour image; cross-modal retrieval; YCbCr colour model; bilateral filtering method.

DOI: 10.1504/IJCISTUDIES.2023.132492

International Journal of Computational Intelligence Studies, 2023 Vol.12 No.1/2, pp.118 - 129

Received: 18 Nov 2022
Accepted: 21 Jan 2023
Published online: 24 Jul 2023 *

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