LIBSVM: A library for support vector machines
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Abstract: LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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TL;DR: A new no-reference (NR) IQA model is developed and a robust image enhancement framework is established based on quality optimization, which can well enhance natural images, low-contrast images,Low-light images, and dehazed images.
Abstract: In this paper, we investigate into the problem of image quality assessment (IQA) and enhancement via machine learning. This issue has long attracted a wide range of attention in computational intelligence and image processing communities, since, for many practical applications, e.g., object detection and recognition, raw images are usually needed to be appropriately enhanced to raise the visual quality (e.g., visibility and contrast). In fact, proper enhancement can noticeably improve the quality of input images, even better than originally captured images, which are generally thought to be of the best quality. In this paper, we present two most important contributions. The first contribution is to develop a new no-reference (NR) IQA model. Given an image, our quality measure first extracts 17 features through analysis of contrast, sharpness, brightness and more, and then yields a measure of visual quality using a regression module, which is learned with big-data training samples that are much bigger than the size of relevant image data sets. The results of experiments on nine data sets validate the superiority and efficiency of our blind metric compared with typical state-of-the-art full-reference, reduced-reference and NA IQA methods. The second contribution is that a robust image enhancement framework is established based on quality optimization. For an input image, by the guidance of the proposed NR-IQA measure, we conduct histogram modification to successively rectify image brightness and contrast to a proper level. Thorough tests demonstrate that our framework can well enhance natural images, low-contrast images, low-light images, and dehazed images. The source code will be released at https://sites.google.com/site/guke198701/publications .
297 citations
Additional excerpts
...module in the proposed BIQME metric [59]....
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TL;DR: This work discusses the relation between-support vector regression (-SVR) and v- support vector regression (v-SVR), and focuses on properties that are different from those of C- Support vector classification (C-SVC) andv-supportvector classification (v -SVC).
Abstract: We discuss the relation between e-support vector regression (e-SVR) and ν-support vector regression (ν-SVR). In particular, we focus on properties that are different from those of C-support vector classification (C-SVC) and ν-support vector classification (ν-SVC). We then discuss some issues that do not occur in the case of classification: the possible range of e and the scaling of target values. A practical decomposition method for ν-SVR is implemented, and computational experiments are conducted. We show some interesting numerical observations specific to regression.
297 citations
09 Oct 2011
TL;DR: The Audio/Visual Emotion Challenge and Workshop (AVEC 2011) is the first competition event aimed at comparison of multimedia processing and machine learning methods for automatic audio, visual and audiovisual emotion analysis, with all participants competing under strictly the same conditions.
Abstract: The Audio/Visual Emotion Challenge and Workshop (AVEC 2011) is the first competition event aimed at comparison of multimedia processing and machine learning methods for automatic audio, visual and audiovisual emotion analysis, with all participants competing under strictly the same conditions. This paper first describes the challenge participation conditions. Next follows the data used - the SEMAINE corpus - and its partitioning into train, development, and test partitions for the challenge with labelling in four dimensions, namely activity, expectation, power, and valence. Further, audio and video baseline features are introduced as well as baseline results that use these features for the three sub-challenges of audio, video, and audiovisual emotion recognition.
296 citations
Cites methods from "LIBSVM: A library for support vecto..."
...For the Video Sub-Challenge, a SVM with a radial basis function (RBF) kernel was used instead implemented in the LibSVM tool [2]....
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TL;DR: The support vector machine recursive feature elimination (SVM-RFE) method for gene selection is enhanced by incorporating a minimum-redundancy maximum-relevancy (MRMR) filter, which provides a framework for combining filter methods and wrapper methods of gene selection.
Abstract: We enhance the support vector machine recursive feature elimination (SVM-RFE) method for gene selection by incorporating a minimum-redundancy maximum-relevancy (MRMR) filter. The relevancy of a set of genes are measured by the mutual information among genes and class labels, and the redundancy is given by the mutual information among the genes. The method improved identification of cancer tissues from benign tissues on several benchmark datasets, as it takes into account the redundancy among the genes during their selection. The method selected a less number of genes compared to MRMR or SVM-RFE on most datasets. Gene ontology analyses revealed that the method selected genes that are relevant for distinguishing cancerous samples and have similar functional properties. The method provides a framework for combining filter methods and wrapper methods of gene selection, as illustrated with MRMR and SVM-RFE methods.
296 citations
TL;DR: A new no-reference (NR)/ blind sharpness metric in the autoregressive (AR) parameter space is established via the analysis of AR model parameters, first calculating the energy- and contrast-differences in the locally estimated AR coefficients in a pointwise way, and then quantifying the image sharpness with percentile pooling to predict the overall score.
Abstract: In this paper, we propose a new no-reference (NR)/ blind sharpness metric in the autoregressive (AR) parameter space. Our model is established via the analysis of AR model parameters, first calculating the energy- and contrast-differences in the locally estimated AR coefficients in a pointwise way, and then quantifying the image sharpness with percentile pooling to predict the overall score. In addition to the luminance domain, we further consider the inevitable effect of color information on visual perception to sharpness and thereby extend the above model to the widely used YIQ color space. Validation of our technique is conducted on the subsets with blurring artifacts from four large-scale image databases (LIVE, TID2008, CSIQ, and TID2013). Experimental results confirm the superiority and efficiency of our method over existing NR algorithms, the state-of-the-art blind sharpness/blurriness estimators, and classical full-reference quality evaluators. Furthermore, the proposed metric can be also extended to stereoscopic images based on binocular rivalry, and attains remarkably high performance on LIVE3D-I and LIVE3D-II databases.
296 citations
References
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TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Abstract: The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.
High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
37,861 citations
"LIBSVM: A library for support vecto..." refers background in this paper
...{1,-1}, C-SVC [Boser et al. 1992; Cortes and Vapnik 1995] solves 4LIBSVM Tools: http://www.csie.ntu.edu.tw/~cjlin/libsvmtools. the following primal optimization problem: l t min 1 w T w +C .i (1) w,b,. 2 i=1 subject to yi(w T f(xi) +b) =1 -.i, .i =0,i =1,...,l, where f(xi)maps xi into a…...
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01 Jan 1998
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Abstract: A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
26,531 citations
"LIBSVM: A library for support vecto..." refers background in this paper
...Under given parameters C > 0and E> 0, the standard form of support vector regression [Vapnik 1998] is ll tt 1 T min w w + C .i + C .i * w,b,.,. * 2 i=1 i=1 subject to w T f(xi) + b- zi = E + .i, zi - w T f(xi) - b = E + .i * , * .i,.i = 0,i = 1,...,l....
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...It can be clearly seen that C-SVC and one-class SVM are already in the form of problem (11)....
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..., l, in two classes, and a vector y ∈ Rl such that yi ∈ {1,−1}, C-SVC (Cortes and Vapnik, 1995; Vapnik, 1998) solves the following primal problem:...
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...Then, according to the SVM formulation, svm train one calls a corresponding subroutine such as solve c svc for C-SVC and solve nu svc for ....
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...Note that b of C-SVC and E-SVR plays the same role as -. in one-class SVM, so we de.ne ....
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01 Jul 1992
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
Abstract: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leave-one-out method and the VC-dimension are given. Experimental results on optical character recognition problems demonstrate the good generalization obtained when compared with other learning algorithms.
11,211 citations
"LIBSVM: A library for support vecto..." refers background in this paper
...It can be clearly seen that C-SVC and one-class SVM are already in the form of problem (11)....
[...]
...Then, according to the SVM formulation, svm train one calls a corresponding subroutine such as solve c svc for C-SVC and solve nu svc for ....
[...]
...Note that b of C-SVC and E-SVR plays the same role as -. in one-class SVM, so we de.ne ....
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...In Section 2, we describe SVM formulations supported in LIBSVM: C-Support Vector Classi.cation (C-SVC), ....
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...{1,-1}, C-SVC [Boser et al. 1992; Cortes and Vapnik 1995] solves 4LIBSVM Tools: http://www.csie.ntu.edu.tw/~cjlin/libsvmtools. the following primal optimization problem: l t min 1 w T w +C .i (1) w,b,. 2 i=1 subject to yi(w T f(xi) +b) =1 -.i, .i =0,i =1,...,l, where f(xi)maps xi into a higher-dimensional space and C > 0 is the regularization parameter....
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01 Jan 2008
TL;DR: A simple procedure is proposed, which usually gives reasonable results and is suitable for beginners who are not familiar with SVM.
Abstract: Support vector machine (SVM) is a popular technique for classication. However, beginners who are not familiar with SVM often get unsatisfactory results since they miss some easy but signicant steps. In this guide, we propose a simple procedure, which usually gives reasonable results.
7,069 citations
"LIBSVM: A library for support vecto..." refers methods in this paper
...A Simple Example of Running LIBSVM While detailed instructions of using LIBSVM are available in the README file of the package and the practical guide by Hsu et al. [2003], here we give a simple example....
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...For instructions of using LIBSVM, see the README file included in the package, the LIBSVM FAQ,3 and the practical guide by Hsu et al. [2003]. LIBSVM supports the following learning tasks....
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TL;DR: Decomposition implementations for two "all-together" multiclass SVM methods are given and it is shown that for large problems methods by considering all data at once in general need fewer support vectors.
Abstract: Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.
6,562 citations