Basis Algorithm

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Kaamran Raahemifar - One of the best experts on this subject based on the ideXlab platform.

  • ICIP - An Efficient Architecture for Entropy-Based Best-Basis Algorithm
    2006 International Conference on Image Processing, 2006
    Co-Authors: S.m. Aroutchelvame, Kaamran Raahemifar
    Abstract:

    In this work, we propose an architecture for the wavelet-packet based best-Basis Algorithm for images using Shannon entropy as cost function. The Algorithm for the logarithm implementation for integers using Taylor series is also proposed to implement Shannon entropy. The proposed architecture includes the architectures for the best-tree selection. The execution time of the proposed hardware for the best-Basis Algorithm for images is compared to its software implementation. The proposed best-Basis architecture has been described in VHDL at the RTL level, simulated successfully for its functional correctness and implemented in an FPGA.

  • An Architecture Design of Threshold-Based Best-Basis Algorithm
    2006 IEEE International Conference on Multimedia and Expo, 2006
    Co-Authors: S.m. Aroutchelvame, Kaamran Raahemifar
    Abstract:

    The best-Basis Algorithm has gained much importance on textured-based image compression and denoising of signals. In this paper, an architecture for the wavelet-packet based best-Basis Algorithm for images is proposed. The paper also describes the architecture for best-tree selection from 2D wavelet packet decomposition. The precision analysis of the proposed architecture is also discussed and the result shows that increase in the precision of input pixel greatly increases the signal-to-noise ratio (SNR) per pixel whereas increase in the precision of filter coefficient does not greatly help in improving the SNR value. The proposed architecture is described in VHDL at the RTL level, simulated successfully for its functional correctness and implemented in an FPGA

  • ICME - An Architecture Design of Threshold-Based Best-Basis Algorithm
    2006 IEEE International Conference on Multimedia and Expo, 2006
    Co-Authors: S.m. Aroutchelvame, Kaamran Raahemifar
    Abstract:

    The best-Basis Algorithm has gained much importance on textured-based image compression and denoising of signals. In this paper, an architecture for the wavelet-packet based best-Basis Algorithm for images is proposed. The paper also describes the architecture for best-tree selection from 2D wavelet packet decomposition. The precision analysis of the proposed architecture is also discussed and the result shows that increase in the precision of input pixel greatly increases the Signal-to-Noise Ratio (SNR) per pixel whereas increase in the precision of filter coefficient does not greatly help in improving the SNR value. The proposed architecture is described in VHDL at the RTL level, simulated successfully for its functional correctness and implemented in an FPGA.

  • An architecture for best-Basis Algorithm using threshold cost function for images
    2006 IEEE International Symposium on Circuits and Systems, 2006
    Co-Authors: S.m. Aroutchelvame, Kaamran Raahemifar
    Abstract:

    Wavelet-packet based best-Basis selection has become a popular method for image compression particularly for the textured images. In this work, we propose architecture for the best Basis Algorithm for images using lifting-based wavelet transform and threshold function as cost function. The architecture for the best-Basis selection from 2D wavelet packet is also described. The proposed architecture has been described in VHDL at the RTL level, simulated successfully for its functional correctness and implemented in FPGA

  • ISCAS - An architecture for best-Basis Algorithm using threshold cost function for images
    2006 IEEE International Symposium on Circuits and Systems, 2006
    Co-Authors: S.m. Aroutchelvame, Kaamran Raahemifar
    Abstract:

    Wavelet-packet based best-Basis selection has become a popular method for image compression particularly for the textured images. In this work, we propose architecture for the best Basis Algorithm for images using lifting-based wavelet transform and threshold function as cost function. The architecture for the best-Basis selection from 2D wavelet packet is also described. The proposed architecture has been described in VHDL at the RTL level, simulated successfully for its functional correctness and implemented in FPGA.

K. Hazaveh - One of the best experts on this subject based on the ideXlab platform.

  • ICIP (1) - Optimized two-dimensional local discriminant Basis Algorithm
    Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), 2003
    Co-Authors: K. Hazaveh, Kaamran Raahemifar
    Abstract:

    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.

  • Optimized local discriminant Basis Algorithm
    2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698), 2003
    Co-Authors: K. Hazaveh, Kaamran Raahemifar
    Abstract:

    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.

  • ICASSP (6) - Optimized local discriminant Basis Algorithm
    2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698), 2003
    Co-Authors: K. Hazaveh, Kaamran Raahemifar
    Abstract:

    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.

  • Local discriminant Basis Algorithm-a review of theory and applications in signal processing
    Proceedings of the 2003 International Symposium on Circuits and Systems 2003. ISCAS '03., 2003
    Co-Authors: K. Hazaveh, Kaamran Raahemifar
    Abstract:

    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.

  • ISCAS (4) - Local discriminant Basis Algorithm-a review of theory and applications in signal processing
    Proceedings of the 2003 International Symposium on Circuits and Systems 2003. ISCAS '03., 2003
    Co-Authors: K. Hazaveh, Kaamran Raahemifar
    Abstract:

    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.

A.s. Willsky - One of the best experts on this subject based on the ideXlab platform.

  • Best Basis Algorithm for signal enhancement
    1995 International Conference on Acoustics Speech and Signal Processing, 1995
    Co-Authors: H.k.s. Mallat, D. Donoho, A.s. Willsky
    Abstract:

    We propose a best Basis Algorithm for signal enhancement in white Gaussian noise. We base our search of best Basis on a criterion of minimal reconstruction error of the underlying signal. We subsequently compare our simple error criterion to the Stein (1981) unbiased risk estimator, and provide a substantiating example to demonstrate its performance. A review is also given of noise removal by thresholding and of wavepacket orthonormal bases.

  • ICASSP - Best Basis Algorithm for signal enhancement
    1995 International Conference on Acoustics Speech and Signal Processing, 1995
    Co-Authors: H.k.s. Mallat, D. Donoho, A.s. Willsky
    Abstract:

    We propose a best Basis Algorithm for signal enhancement in white Gaussian noise. We base our search of best Basis on a criterion of minimal reconstruction error of the underlying signal. We subsequently compare our simple error criterion to the Stein (1981) unbiased risk estimator, and provide a substantiating example to demonstrate its performance. A review is also given of noise removal by thresholding and of wavepacket orthonormal bases.

  • Robust multiscale representation of processes and optimal signal reconstruction
    Proceedings of IEEE-SP International Symposium on Time- Frequency and Time-Scale Analysis, 1994
    Co-Authors: H. Krim, J.-c. Pesquet, A.s. Willsky
    Abstract:

    We propose a statistical approach to obtain a "best Basis" representation of an observed random process. We derive statistical properties of a criterion first proposed to determine the best wavelet packet Basis, and, proceed to use it in constructing a statistically sound Algorithm. For signal enhancement, this best Basis Algorithm is followed by a nonlinear filter based on the minimum description length (MDL) criterion. We show that it is equivalent to a min-max based Algorithm proposed by Donoho and Johnstone (1992).

Mariusz Ziolko - One of the best experts on this subject based on the ideXlab platform.

  • IIH-MSP - Mean Best Basis Algorithm for Wavelet Speech Parameterization
    2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009
    Co-Authors: Jakub Galka, Mariusz Ziolko
    Abstract:

    In this paper, we propose a feature selection and transformation approach for universal steganalysis based on Genetic Algorithm (GA) and higher order statistics. We choose three types of typical statistics as candidate features and twelve kinds of basic functions as candidate transformations. The GA is utilized to select a subset of candidate features, a subset of candidate transformations and coefficients of the Logistic Regression Model for blind image steganalysis. The Logistic Regression Model is then used as the classifier. Experimental results show that the GA based approach increases the blind detection accuracy and also provides a good generality by identifying an untrained stego-Algorithm. *

  • NOLISP - Wavelet speech feature extraction using mean best Basis Algorithm
    Advances in Nonlinear Speech Processing, 2009
    Co-Authors: Jakub Galka, Mariusz Ziolko
    Abstract:

    This paper presents Mean Best Basis Algorithm, an extension of the well known Best Basis Wickerhouser's method, for an adaptive wavelet decomposition of variable-length signals. A novel approach is used to obtain a decomposition tree of the wavelet-packet cosine hybrid transform for speech signal feature extraction. Obtained features are tested using the Polish language hidden Markov model phone classifier.

  • Mean Best Basis Algorithm for Wavelet Speech Parameterization
    2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009
    Co-Authors: Jakub Galka, Mariusz Ziolko
    Abstract:

    In this paper, we propose a feature selection and transformation approach for universal steganalysis based on genetic Algorithm (GA) and higher order statistics. We choose three types of typical statistics as candidate features and twelve kinds of basic functions as candidate transformations. The GA is utilized to select a subset of candidate features, a subset of candidate transformations and coefficients of the logistic regression model for blind image steganalysis. The logistic regression model is then used as the classifier. Experimental results show that the GA based approach increases the blind detection accuracy and also provides a good generality by identifying an untrained stego-Algorithm.

  • Best Basis selection of the Wavelet Packet Cosine Transform in speech analysis
    AFRICON 2009, 2009
    Co-Authors: Jakub Galka, Mariusz Ziolko
    Abstract:

    In this paper a new application of the wavelet packet cosine transform (WPCT), used in the adaptive wavelet parameterization scheme, is presented. This is an extension of the best Basis Algorithm. Obtained optimized wavelet decomposition schemes are used for speech feature extraction and are tested using Polish language hidden Markov model (HMM) phone-classifier.

Trieu-kien Truong - One of the best experts on this subject based on the ideXlab platform.

  • Automatic music genre classification based on wavelet package transform and best Basis Algorithm
    2012 IEEE International Symposium on Circuits and Systems (ISCAS), 2012
    Co-Authors: Shih-hao Chen, Shi-huang Chen, Trieu-kien Truong
    Abstract:

    In this paper, an improved music genre classification method is presented. The proposed method makes use of the wavelet package transform (WPT) and the best Basis Algorithm (BBA) to accurately classify and increase classification performance. It is well known that WPT can generate a wavelet decomposition that offers a richer signal analysis. In this paper, the music signal is first decomposed into approximation and detail coefficients using WPT with the best Basis Algorithm to minimize the Shannon entropy and maximize the representation of music signal. This paper uses the Top-Down search strategy with cost function to select the best Basis. Then the proposed method could apply support vector machine (SVM) to build a music genre classifier using the mel-frequency cepstral coefficients (MFCC) and log energies extracted from the decomposition coefficients of WPT with the best Basis Algorithm. Finally one can perform music genre classification with the built music genre classifier. Experiments conducted on three different music datasets have shown that the proposed method can achieve higher classification accuracy than other music genre classification methods with the same experimental setup.

  • ISCAS - Automatic music genre classification based on wavelet package transform and best Basis Algorithm
    2012 IEEE International Symposium on Circuits and Systems, 2012
    Co-Authors: Shih-hao Chen, Shi-huang Chen, Trieu-kien Truong
    Abstract:

    In this paper, an improved music genre classification method is presented. The proposed method makes use of the wavelet package transform (WPT) and the best Basis Algorithm (BBA) to accurately classify and increase classification performance. It is well known that WPT can generate a wavelet decomposition that offers a richer signal analysis. In this paper, the music signal is first decomposed into approximation and detail coefficients using WPT with the best Basis Algorithm to minimize the Shannon entropy and maximize the representation of music signal. This paper uses the Top-Down search strategy with cost function to select the best Basis. Then the proposed method could apply support vector machine (SVM) to build a music genre classifier using the mel-frequency cepstral coefficients (MFCC) and log energies extracted from the decomposition coefficients of WPT with the best Basis Algorithm. Finally one can perform music genre classification with the built music genre classifier. Experiments conducted on three different music datasets have shown that the proposed method can achieve higher classification accuracy than other music genre classification methods with the same experimental setup.