Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Dr. Dobb's Journal
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Detecting the dominant points by the curvature-based polygonal approximation
CVGIP: Graphical Models and Image Processing
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Gabor wavelets for statistical pattern recognition
The handbook of brain theory and neural networks
Shape Analysis and Classification: Theory and Practice
Shape Analysis and Classification: Theory and Practice
Multiscale Fractal Characterization of Three-Dimensional Gene Expression Data
SIBGRAPI '02 Proceedings of the 15th Brazilian Symposium on Computer Graphics and Image Processing
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Recognition of Shapes by Editing Their Shock Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
2D Euclidean distance transform algorithms: A comparative survey
ACM Computing Surveys (CSUR)
Fractal dimension applied to plant identification
Information Sciences: an International Journal
Shape classification using complex network and Multi-scale Fractal Dimension
Pattern Recognition Letters
Representation of functional data in neural networks
Neurocomputing
Regularization in matrix relevance learning
IEEE Transactions on Neural Networks
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Texture analysis by multi-resolution fractal descriptors
Expert Systems with Applications: An International Journal
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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.