Open Access
ARTICLE
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.
Keywords
Cite This Article
Wang, X., Zhu, Z., Hua, Z. (2023). Refined Sparse Representation Based Similar Category Image Retrieval.
CMES-Computer Modeling in Engineering & Sciences, 134(2), 893–908.