Advanced search
Start date
Betweenand
Related content
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

A comparative study on multiscale fractal dimension descriptors

Full text
Author(s):
Florindo, J. B. [1] ; Backes, A. R. [2] ; de Castro, M. [3] ; Bruno, O. M. [1]
Total Authors: 4
Affiliation:
[1] USP, IFSC, BR-13560970 Sao Carlos, SP - Brazil
[2] Univ Fed Uberlandia, Fac Comp, BR-38408100 Uberlandia, MG - Brazil
[3] USP, Inst Ciencias Matemat & Comp, BR-13560970 Sao Carlos, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: PATTERN RECOGNITION LETTERS; v. 33, n. 6, p. 798-806, APR 15 2012.
Web of Science Citations: 23
Abstract

Fractal theory presents a large number of applications to image and signal analysis. Although the fractal dimension can be used as an image object descriptor, a multiscale approach, such as multiscale fractal dimension (MFD), increases the amount of information extracted from an object. MFD provides a curve which describes object complexity along the scale. However, this curve presents much redundant information, which could be discarded without loss in performance. Thus, it is necessary the use of a descriptor technique to analyze this curve and also to reduce the dimensionality of these data by selecting its meaningful descriptors. This paper shows a comparative study among different techniques for MFD descriptors generation. It compares the use of well-known and state-of-the-art descriptors, such as Fourier, Wavelet, Polynomial Approximation (PA), Functional Data Analysis (FDA), Principal Component Analysis (PCA), Symbolic Aggregate Approximation (SAX), kernel PCA, Independent Component Analysis (ICA), geometrical and statistical features. The descriptors are evaluated in a classification experiment using Linear Discriminant Analysis over the descriptors computed from MFD curves from two data sets: generic shapes and rotated fish contours. Results indicate that PCA, FDA, PA and Wavelet Approximation provide the best MFD descriptors for recognition and classification tasks. (C) 2012 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 11/01523-1 - Computer vision methods applied to the identification and analysis of plants
Grantee:Odemir Martinez Bruno
Support Opportunities: Regular Research Grants
FAPESP's process: 10/09074-9 - Long-term survival models with measurement errors
Grantee:Mario de Castro Andrade Filho
Support Opportunities: Scholarships abroad - Research