The Experts below are selected from a list of 20373 Experts worldwide ranked by ideXlab platform

Tsuhan Chen - One of the best experts on this subject based on the ideXlab platform.

  • reinterpreting the application of gabor filters as a manipulation of the margin in linear support vector machines
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010
    Co-Authors: Ahmed Bilal Ashraf, Simon Lucey, Tsuhan Chen
    Abstract:

    Linear filters are ubiquitously used as a Preprocessing Step for many classification tasks in computer vision. In particular, applying Gabor filters followed by a classification stage, such as a support vector machine (SVM), is now common practice in computer vision applications like face identity and expression recognition. A fundamental problem occurs, however, with respect to the high dimensionality of the concatenated Gabor filter responses in terms of memory requirements and computational efficiency during training and testing. In this paper, we demonstrate how the Preprocessing Step of applying a bank of linear filters can be reinterpreted as manipulating the type of margin being maximized within the linear SVM. This new interpretation leads to sizable memory and computational advantages with respect to existing approaches. The reinterpreted formulation turns out to be independent of the number of filters, thereby allowing the examination of the feature spaces derived from arbitrarily large number of linear filters, a hitherto untestable prospect. Further, this new interpretation of filter banks gives new insights, other than the often cited biological motivations, into why the Preprocessing of images with filter banks, like Gabor filters, improves classification performance.

Degang Chen - One of the best experts on this subject based on the ideXlab platform.

  • a systematic study on attribute reduction with rough sets based on general binary relations
    Information Sciences, 2008
    Co-Authors: Changzhong Wang, Degang Chen
    Abstract:

    Attribute reduction is considered as an important Preprocessing Step for pattern recognition, machine learning, and data mining. This paper provides a systematic study on attribute reduction with rough sets based on general binary relations. We define a relation information system, a consistent relation decision system, and a relation decision system and their attribute reductions. Furthermore, we present a judgment theorem and a discernibility matrix associated with attribute reduction in each type of system; based on the discernibility matrix, we can compute all the reducts. Finally, the experimental results with UCI data sets show that the proposed reduction methods are an effective technique to deal with complex data sets.

Jocelyn Chanussot - One of the best experts on this subject based on the ideXlab platform.

  • noise reduction in hyperspectral imagery overview and application
    Remote Sensing, 2018
    Co-Authors: Behnood Rasti, Paul Scheunders, Pedram Ghamisi, Giorgio Licciardi, Jocelyn Chanussot
    Abstract:

    Hyperspectral remote sensing is based on measuring the scattered and reflected electromagnetic signals from the Earth’s surface emitted by the Sun. The received radiance at the sensor is usually degraded by atmospheric effects and instrumental (sensor) noises which include thermal (Johnson) noise, quantization noise, and shot (photon) noise. Noise reduction is often considered as a Preprocessing Step for hyperspectral imagery. In the past decade, hyperspectral noise reduction techniques have evolved substantially from two dimensional bandwise techniques to three dimensional ones, and varieties of low-rank methods have been forwarded to improve the signal to noise ratio of the observed data. Despite all the developments and advances, there is a lack of a comprehensive overview of these techniques and their impact on hyperspectral imagery applications. In this paper, we address the following two main issues; (1) Providing an overview of the techniques developed in the past decade for hyperspectral image noise reduction; (2) Discussing the performance of these techniques by applying them as a Preprocessing Step to improve a hyperspectral image analysis task, i.e., classification. Additionally, this paper discusses about the hyperspectral image modeling and denoising challenges. Furthermore, different noise types that exist in hyperspectral images have been described. The denoising experiments have confirmed the advantages of the use of low-rank denoising techniques compared to the other denoising techniques in terms of signal to noise ratio and spectral angle distance. In the classification experiments, classification accuracies have improved when denoising techniques have been applied as a Preprocessing Step.

Ahmed Bilal Ashraf - One of the best experts on this subject based on the ideXlab platform.

  • reinterpreting the application of gabor filters as a manipulation of the margin in linear support vector machines
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010
    Co-Authors: Ahmed Bilal Ashraf, Simon Lucey, Tsuhan Chen
    Abstract:

    Linear filters are ubiquitously used as a Preprocessing Step for many classification tasks in computer vision. In particular, applying Gabor filters followed by a classification stage, such as a support vector machine (SVM), is now common practice in computer vision applications like face identity and expression recognition. A fundamental problem occurs, however, with respect to the high dimensionality of the concatenated Gabor filter responses in terms of memory requirements and computational efficiency during training and testing. In this paper, we demonstrate how the Preprocessing Step of applying a bank of linear filters can be reinterpreted as manipulating the type of margin being maximized within the linear SVM. This new interpretation leads to sizable memory and computational advantages with respect to existing approaches. The reinterpreted formulation turns out to be independent of the number of filters, thereby allowing the examination of the feature spaces derived from arbitrarily large number of linear filters, a hitherto untestable prospect. Further, this new interpretation of filter banks gives new insights, other than the often cited biological motivations, into why the Preprocessing of images with filter banks, like Gabor filters, improves classification performance.

Nicholas J Roseveare - One of the best experts on this subject based on the ideXlab platform.