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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Enhancing texture descriptors by a neighborhood approach to the non-additive entropy

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Florindo, Joao Batista [1] ; Assirati, Lucas [1] ; Bruno, Odemir Martinez [1]
Total Authors: 3
[1] Univ Sao Paulo, Sao Carlos Inst Phys, Sci Comp Grp, BR-13560970 Sao Carlos, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Source: DIGITAL SIGNAL PROCESSING; v. 44, p. 14-25, SEP 2015.
Web of Science Citations: 1

This work proposes to enhance well-known descriptors of texture images by extracting such descriptors both directly from pixel intensities as well as from the local non-additive entropy of the image. The method can be divided into four steps. 1) The descriptors are computed for the original image according to what is described in the literature. 2) The image is transformed by computing the non-additive entropy at each pixel, considering its neighborhood. 3) Similarly to step 1, the descriptors are computed from the transformed image. 4) Descriptors from the original and transformed images are combined by means of a Karhunen-Loeve transform. Four texture descriptors widely used in the literature were considered: Gabor wavelets, Gray-Level Co-occurrence Matrix, Local Binary Patterns and Bouligand-Minkowski fractal descriptors. The proposal is assessed by comparing the performance of the descriptors alone and after combined with the non-additive entropy. The results demonstrate that the combination achieved the best results both in image retrieval and classification tasks. The entropy is still more efficient in local-based methods: Local Binary Patterns and Gray-Level Co-occurrence Matrix. (C) 2015 Elsevier Inc. All rights reserved. (AU)

FAPESP's process: 13/22205-3 - Tissue spatial organization and fractal analyses for automated plant taxonomy
Grantee:Joao Batista Florindo
Support type: Scholarships abroad - Research Internship - Post-doctor
FAPESP's process: 11/01523-1 - Computer vision methods applied to the identification and analysis of plants
Grantee:Odemir Martinez Bruno
Support type: Regular Research Grants
FAPESP's process: 12/19143-3 - Fractal Geometry and Image Analysis Applied to Vegetal Biology
Grantee:Joao Batista Florindo
Support type: Scholarships in Brazil - Post-Doctorate