In the existing tolerance granular space models, grid points in each layer are established only taking space position into account, which ignore uncertainties of image texture, such as randomness, fuzziness and relevance. As a matter of fact, it is very important to extract grid points for constructing tolerance granular space, which are tolerance granules’ position or center. Therefore, it is very meaningful for the accurate texture feature-description of images to extract grid points well. To address this issue, we firstly apply cloud model to extracting grid points, and establish two new tolerance granular space models. Then, similarity measures based on cloud model and tolerance granule space are presented and two novel image retrieval methods are introduced, including an image texture recognition and a color image retrieval method. Finally, simulation experiments are done on images of image test set chosen from Corel Database, to compare our proposed methods with the conventional color histogram-based image retrieval method, the salient regions and nonsubsampled contourlet transform-based image retrieval method, and tolerance granular-based multi-level texture image retrieval method. The experimental results demonstrate that the proposed methods are indeed efficient and of practical value to many real-world problems.
Digital Object Identifier (DOI)
Xu, Jiucheng; Ren, Jinyu; Sun, Lin; and Xu, Tianhe
"Cloud Model and Tolerance Granular Space-based Image Retrieval Methods,"
Applied Mathematics & Information Sciences: Vol. 08:
6, Article 56.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol08/iss6/56