Open Access

ARTICLE

Refined Sparse Representation Based Similar Category Image Retrieval

Xin Wang, Zhilin Zhu, Zhen Hua^*

School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, China

* Corresponding Author: Zhen Hua. Email:

(This article belongs to the Special Issue: Data Acquisition and Electromagnetic Interference Detection by Internet of Things)

Computer Modeling in Engineering & Sciences 2023, 134(2), 893-908. https://doi.org/10.32604/cmes.2022.021287

Received 06 January 2022; Accepted 14 March 2022; Issue published 31 August 2022

Abstract

Given one specific image, it would be quite significant if humanity could simply retrieve all those pictures that fall into a similar category of images. However, traditional methods are inclined to achieve high-quality retrieval by utilizing adequate learning instances, ignoring the extraction of the image’s essential information which leads to difficulty in the retrieval of similar category images just using one reference image. Aiming to solve this problem above, we proposed in this paper one refined sparse representation based similar category image retrieval model. On the one hand, saliency detection and multi-level decomposition could contribute to taking salient and spatial information into consideration more fully in the future. On the other hand, the cross mutual sparse coding model aims to extract the image’s essential feature to the maximum extent possible. At last, we set up a database concluding a large number of multi-source images. Adequate groups of comparative experiments show that our method could contribute to retrieving similar category images effectively. Moreover, adequate groups of ablation experiments show that nearly all procedures play their roles, respectively.

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Keywords

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