| Full text | |
| Author(s): |
Total Authors: 2
|
| Affiliation: | [1] Univ Estadual Campinas, Inst Math Stat & Sci Comp, Rua Sergio Buarque de Holanda 651, BR-13083859 Campinas, SP - Brazil
Total Affiliations: 1
|
| Document type: | Journal article |
| Source: | PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS; v. 545, MAY 1 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
This work proposes a new model based on fractal descriptors for the classification of grayscale texture images. The method consists of scanning the image with a sliding box and collecting statistical information about the pixel distribution. Varying the box size, an estimation of the fractality of the image can be obtained at different scales, providing a more complete description of how such parameter changes in each image. The same strategy is also applied to a especial encoding of the image based on local binary patterns. Descriptors both from the original image and from the local encoding are combined to provide even more precise and robust results in image classification. A statistical model based on the theory of sliding window detection probabilities and Markov transition processes is formulated to explain the effectiveness of the method. The descriptors were tested on the identification of Brazilian plant species using scanned images of the leaf surface. The classification accuracy was also verified on three benchmark databases (KTH-TIPS2-b, UIUC and UMD). The results obtained demonstrate the power of the proposed approach in texture classification and, in particular, in the practical problem of plant species identification. (C) 2019 Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 16/16060-0 - Pattern Recognition on Images Based on Complex Systems |
| Grantee: | Joao Batista Florindo |
| Support Opportunities: | Regular Research Grants |