Title: Cross-modal retrieval of large-scale images in social media based on spatial distribution entropy

Authors: Jie Ding; Guotao Zhao; Fang Xu

Addresses: College of Technology, Hubei Engineering University, Xiaogan, China ' School of Foreign Languages, Hubei Engineering University, Xiaogan, China ' School of Computer and Information Science, Hubei Engineering University, Xiaogan, China

Abstract: In order to improve the cross-modal retrieval accuracy of large-scale social media images, a cross-modal retrieval method for large-scale social media images based on spatial distribution entropy is proposed. First, extract the information features of the colour and texture of the image. Then, use the image cross-modal retrieval method based on the spatial distribution entropy to calculate the spatial distribution entropy of the colour information and texture information features in the image. Finally, use the Euclidean distance to judge the space between social media images. The matching degree of the distribution entropy, according to the matching degree, is used to judge whether the image cross-modal retrieval is successful or not. The experimental results verify that the proposed method can implement comprehensive retrieval according to the specific characteristics of the retrieved images, and the matching degree of image retrieval is greater than 95%, and the retrieval accuracy is high.

Keywords: spatial distribution entropy; social media; large-scale; cross-modal; image; retrieval.

DOI: 10.1504/IJWBC.2024.136649

International Journal of Web Based Communities, 2024 Vol.20 No.1/2, pp.88 - 101

Received: 03 Mar 2022
Accepted: 09 Jun 2022
Published online: 15 Feb 2024 *

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