Refined Sparse Representation Based Similar Category Image Retrieval

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.