A comparative study on multiscale fractal dimension descriptors

Autor(es)

Florindo, J. B. ^[1] ; Backes, A. R. ^[2] ; de Castro, M. ^[3] ; Bruno, O. M. ^[1]

Afiliação do(s) autor(es)

^[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

• Tipo de documento: Artigo Científico
• Fonte: PATTERN RECOGNITION LETTERS; v. 33, n. 6, p. 798-806, APR 15 2012.
• Citações Web of Science: 23

Resumo

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)

• Processo FAPESP: 10/09074-9 - Modelos de sobrevivência com fração de cura com erros nas variáveis explicativas
• Beneficiário: Mario de Castro Andrade Filho
• Linha de fomento: Bolsas no Exterior - Pesquisa
• Processo FAPESP: 11/01523-1 - Métodos de visão computacional aplicados à identificação e análise de plantas
• Beneficiário: Odemir Martinez Bruno
• Linha de fomento: Auxílio à Pesquisa - Regular