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Texture classification using improved 2D local discriminant basis

Texture classification using improved 2D local discriminant ...

引用

IEEE/SP Workshop on Statistical Signal Processing (SSP)

作者： M. Sabri J. Alirezaie Department of Electrical and Computer Engineering Ryerson University Department of System Design Engineering University of Waterloo Canada Department of Electrical and Computer Engineering Ryerson University Canada

Local discriminant basis (LDB) is a supervised feature extraction method. Since LDB uses a redundant dictionary of wavelet packet basis, it is an excellent feature extractor for underlying features on time-frequency plane. Extraction of relevant features is a very important issue in signal and image classification. This reduces the computation complexity , while increasing the classification accuracy in the absence of adequate training samples. LDB algorithm utilizes best basis algorithm to find most discrimination basis among orthonormal redundant basis provided by wavelet packet transform. The classification performance of LDB algorithm is further improved using optimally weighted features. The genetic algorithm is utilized to find the optimal weight coefficients. The optimization step decreases within-class variance of training textures and increases between-class distances of teacher textures. The proposed algorithm is then tested on problem of composite texture segmentation. The results are compared with the weight coefficients obtained from Euclidean distance, J-divergence and Hellinger distance. Misclassification error is reduced by 26%-31%.

关键词： Feature extraction Wavelet packets Dictionaries Time frequency analysis Image classification basis algorithms Wavelet transforms Genetic algorithms Testing Euclidean distance

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Optimized local discriminant basis algorithm

Optimized local discriminant basis algorithm

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： K. Hazaveh K. Raahemifar Electrical & Computer Engineering Department Ryerson University Toronto Canada

Local discriminant bases method is a powerful algorithmic framework for feature extraction and classification applications that is based on supervised training. It is considerably faster compared to more theoretically ideal feature extraction methods such as principal component analysis or projection pursuit. In this paper an optimization block is added to the original local discriminant bases algorithm to promote the difference between disjoint signal classes. This is done by optimally weighting the local discriminant basis using the steepest decent algorithm. The proposed method is particularly useful when background features in the signal space show strong correlation with regions of interest in the signal as in mammograms for instance.

关键词： basis algorithms Feature extraction Time frequency analysis Dictionaries Karhunen-Loeve transforms Principal component analysis Wavelet packets Computational efficiency Noise reduction Pattern classification

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Two-dimensional local discriminant basis algorithm for texture classification

Two-dimensional local discriminant basis algorithm for textu...

引用

Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： M. Sabri K. Hazaveh J. Alirezaie K. Raahemifar Department of Electrical and Computer Engineering Ryerson University Canada

Local discriminant basis (LDB) is known as a powerful and efficient feature extractor for signal classification. As LDB is based on wavelet transform it can effectively represent a nonstationary signal with a few number of significant coefficients. On the other hand the redundant orthogonal basis which comes with wavelet packet transform, makes it quite attractive for classification. In this paper the LDB method is extended to two-dimension and applied to texture classification problem. For this purpose three 256 /spl times/ 256 texture images from Brodatz (1968) are selected. Each Image is divided to 2 equal subimages. From each half 64 /spl times/ 64 subimages are selected after randomly finding a top-left point pixel. The miss-classification rate of 20%-30% is obtained.

关键词： basis algorithms Feature extraction Signal processing algorithms Wavelet transforms Wavelet packets Iron Data mining Frequency Principal component analysis Java

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Optimized two-dimensional local discriminant basis algorithm

Optimized two-dimensional local discriminant basis algorithm

引用

IEEE International Conference on Image Processing

作者： K. Hazaveh K. Raahemifar Sunnybrook & Womens College Health Sciences Centre Department of Medical Biophysics University of Toronto Canada Electrical & Computer Engineering Department Ryerson University Toronto ONT Canada

Local discriminant basis algorithm (LDB) is a supervised scheme for feature extraction and nonstationary signal classification. Due to its fast computational time, O(n log n), and excellent time-frequency localization, it is a promising method for nonstationary signal analysis. An optimized version of local discriminant basis (OLDB) has been recently proposed that emphasizes certain regions of interest in different classes using initial LDB features. The optimization process is particularly useful when background structures show high correlation with desired features in signal or image space as in mammograms. In this paper the performance of OLDB algorithm is studied in a texture classification problem. Classification into more than two classes of signals or images is a challenging problem and OLDB is capable of obtaining 85% accuracy classifying grayscale 64/spl times/64 textured images into three classes using only the 280 top LDB features as studied in this paper.

关键词： basis algorithms Signal analysis Signal processing algorithms Signal processing Matching pursuit algorithms Feature extraction Time frequency analysis Wavelet analysis Wavelet packets Principal component analysis

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Optimized local discriminant basis algorithm

Optimized local discriminant basis algorithm

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： K. Hazaveh K. Raahemifar Electrical & Computer Engineering Department Ryerson University Toronto Canada

Local discriminant bases method is a powerful algorithmic framework for feature extraction and classification applications that is based on supervised training. It is considerably faster compared to more theoretically ideal feature extraction methods such as principal component analysis or projection pursuit. In this paper an optimization block is added to original local discriminant bases algorithm to promote the difference between disjoint signal classes. This is done by optimally weighting the local discriminant basis using steepest decent algorithm. The proposed method is particularly useful when background features in the signal space show strong correlation with regions of interest in the signal as in mammograms for instance.

关键词： basis algorithms Karhunen-Loeve transforms Feature extraction Principal component analysis Time frequency analysis Dictionaries Wavelet packets Mammography Pattern classification Computational efficiency

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Local discriminant basis algorithm-a review of theory and applications in signal processing

Local discriminant basis algorithm-a review of theory and ap...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： K. Hazaveh K. Raahemifar Electrical and Computer Engineering Department Ryerson University Toronto ONT Canada

Local discriminant basis (LDB) algorithm is a powerful algorithmic framework that was originally developed by Coifman and Saito as a technique for analyzing object classification problems. Prior to the development of LDB, an adapted waveform framework called best basis algorithm had been developed mainly for signal compression problems. The main advantage of LDB over other similar techniques such as Karhunen-Loeve transform (KLT), also known as principal component analysis (PCA), is its lower computational cost of O(n log n) order. This paper is the outcome of a literature review on theory and applications of LDB in signal processing.

关键词： basis algorithms Signal processing algorithms Karhunen-Loeve transforms Image coding Principal component analysis Computational efficiency Wavelet packets Noise reduction Application software Feature extraction

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Dysphonic voice classification using wavelet packet transform and artificial neural network

Dysphonic voice classification using wavelet packet transfor...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： A. Schuck L.V. Guimaraes J.O. Wisbeck Department of Electrical Engineering Universidade Federal do Rio Grande do Sul Porto Alegre RS Brazil

In [Schuck Jr. and Parraga, 2002] work, it was demonstrated the viability of the wavelet packet transform (WP) and the best basis algorithm (BBA) as a feature extractor (FE) for a dysphonic voice classification systems. It was shown the better choices of wavelet and cost functions were Symlet 5 and Shannon entropy. Also, a linear discriminator between normal and dysphonic voices was performed. This work present the use an artificial neural network (ANN) in addition to WP and BBA to perform a non-linear discriminator. The WP with 5 dilatation levels and BBA of the sustained vowel /a/ of 13 normal and 51 dysphonic previously diagnosed subjects were performed. Then the entropy values of each best tree's nodes were used for the classification. A ANN was designed with 3 layers ( 4 neurones in the hidden layer and 2 in the last layer). The non-linear function was hyperbolic tangent. The ANN was trained using backpropagation with a group of 6 normal and 21 dysphonic subjects chose at random from the database. Then, all the 61 subjects were classified. The system had a success rate 84.3%, with 4.6% of false negatives and 10.9% false positives.

关键词： Wavelet packets Wavelet transforms Artificial neural networks Entropy basis algorithms Feature extraction Iron Cost function Classification tree analysis Backpropagation

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Efficient decoding of (binary) cyclic codes above the correction capacity of the code using grobner bases

Efficient decoding of (binary) cyclic codes above the correc...

引用

IEEE International Symposium on Information Theory

作者： D. Augot M. Bardet J.-C. Faugere Domaine de Voluceau INRIA Rocquencourt Le Chesnay France CNRS UPMC INRIA Projet SPACES LIPG LORIA Paris France

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Expanding a behavioral repertoire by tuning spinal primitives

Expanding a behavioral repertoire by tuning spinal primitive...

引用

International IEEE/EMBS Conference on Neural Engineering, CNE

作者： S.F. Giszter C.B. Hart School of Bioengineering Drexel University PA USA Neurobiology and Anatomy College of Medicine PA USA

Work in the frog spinal cord, and more recently in mammals, suggests that the degrees of freedom problem in motor planning may be simplified by building motor actions from combinations of motor primitives. How motor primitives arise from spinal circuits, their properties and their plasticity are important issues for this framework. Most recently, we have used various decomposition techniques (independent components analysis, matching pursuit cosine packet analysis, best basis algorithm and wavelet methods) in both simple and complex behaviors to examine the muscles that are controlled as groups or units, and to investigate the pattern and time scale of action of primitives in behaviors. Our data are beginning to provide a description of the way in which spinal elements are controlled for incorporation into more complex behaviors. We outline this description and our methods in this paper.

关键词： Independent component analysis Circuit optimization Spinal cord Matching pursuit algorithms Pattern analysis Algorithm design and analysis basis algorithms Wavelet analysis Wavelet packets Muscles

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Computer algebra tailored to matrix inequalities in control

Computer algebra tailored to matrix inequalities in control

引用

IEEE Conference on Decision and Control

作者： J.W. Helton M.C. de Oliveira School of Electrical and Computer Engineering State University of Campinas-UNICAMP Sao Paulo Brazil Department of Mathematics University of California San Diego La Jolla CA USA

A major advance in linear systems theory has been a formalism for converting systems problems to matrix inequalities. We describe computer algebra algorithms, methodology, and implementation which allows users to convert many systems problems to linear matrix inequalities. We focus on computer algebra methodology which can assist with the well known change of variables method for producing LMIs introduced by Scherer, Gahinet and Chilali.

关键词： Algebra Matrices Linear matrix inequalities Matrix converters Control systems basis algorithms Linear systems Mathematics Packaging Software algorithms

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