TEXTURE SEGMENTATION USING SEMI-SUPERVISED SUPPORT VECTOR MACHINES

2004 ◽  
Vol 04 (02) ◽  
pp. 131-142 ◽  
Author(s):  
SAEID SANEI
Keyword(s):  
Support Vector Machines ◽  
High Spatial Frequency ◽  
Misclassification Error ◽  
Support Vector ◽  
Frequency Noise ◽  
Multiple Constraints ◽  
Training Set ◽  
Vector Machines ◽  
Segmentation Algorithms ◽  
A Minor

Segmentation of natural textures has been investigated by developing a novel semi-supervised support vector machines (S3VM) algorithm with multiple constraints. Unlike conventional segmentation algorithms the proposed method does not classify the textures but classifies the uniform-texture regions and the regions of boundaries. Also the overall algorithm does not use any training set as used by all other learning algorithms such as conventional SVMs. During the process, the images are restored from high spatial frequency noise. Then various-order statistics of the textures within a sliding two-dimensional window are measured. K-mean algorithm is used to initialise the clustering procedure by labelling part of the class members and the classifier parameters. Therefore at this stage we have both the training and the working sets. A non-linear S3VM is then developed to exploit both sets to classify all the regions. The convex algorithm maximises a defined cost function by incorporating a number of constraints. The algorithm has been applied to combinations of a number of natural textures. It is demonstrated that the algorithm is robust, with negligible misclassification error. However, for complex textures there may be a minor misplacement of the edges.

scholarly journals Cascade Support Vector Machines with Dimensionality Reduction

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Oliver Kramer
Keyword(s):  
Support Vector Machines ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Large Data ◽  
Locally Linear Embedding ◽  
Benchmark Problems ◽  
Support Vector ◽  
Training Set ◽  
Support Vectors ◽  
Vector Machines

Cascade support vector machines have been introduced as extension of classic support vector machines that allow a fast training on large data sets. In this work, we combine cascade support vector machines with dimensionality reduction based preprocessing. The cascade principle allows fast learning based on the division of the training set into subsets and the union of cascade learning results based on support vectors in each cascade level. The combination with dimensionality reduction as preprocessing results in a significant speedup, often without loss of classifier accuracies, while considering the high-dimensional pendants of the low-dimensional support vectors in each new cascade level. We analyze and compare various instantiations of dimensionality reduction preprocessing and cascade SVMs with principal component analysis, locally linear embedding, and isometric mapping. The experimental analysis on various artificial and real-world benchmark problems includes various cascade specific parameters like intermediate training set sizes and dimensionalities.

scholarly journals Properties of Support Vector Machines

1998 ◽  
Vol 10 (4) ◽  
pp. 955-974 ◽  
Author(s):  
Massimiliano Pontil ◽  
Alessandro Verri
Keyword(s):  
Support Vector Machines ◽  
Finite Dimension ◽  
Regularization Parameter ◽  
Feature Space ◽  
Support Vector ◽  
Training Set ◽  
Mathematical Properties ◽  
Support Vectors ◽  
Vector Machines ◽  
Almost All

Support vector machines (SVMs) perform pattern recognition between two point classes by finding a decision surface determined by certain points of the training set, termed support vectors (SV). This surface, which in some feature space of possibly infinite dimension can be regarded as a hyperplane, is obtained from the solution of a problem of quadratic programming that depends on a regularization parameter. In this article, we study some mathematical properties of support vectors and show that the decision surface can be written as the sum of two orthogonal terms, the first depending on only the margin vectors (which are SVs lying on the margin), the second proportional to the regularization parameter. For almost all values of the parameter, this enables us to predict how the decision surface varies for small parameter changes. In the special but important case of feature space of finite dimension m, we also show that there are at most m + 1 margin vectors and observe that m + 1 SVs are usually sufficient to determine the decision surface fully. For relatively small m, this latter result leads to a consistent reduction of the SV number.

scholarly journals Toward Accelerated Training of Parallel Support Vector Machines Based on Voronoi Diagrams

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1605
Author(s):  
Cesar Alfaro ◽  
Javier Gomez ◽  
Javier M. Moguerza ◽  
Javier Castillo ◽  
Jose I. Martinez
Keyword(s):  
Support Vector Machines ◽  
Data Exchange ◽  
Smart Cities ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Training Set ◽  
Vector Machines ◽  
Comparable Performance ◽  
Novel Method ◽  
Classification Tasks

Typical applications of wireless sensor networks (WSN), such as in Industry 4.0 and smart cities, involves acquiring and processing large amounts of data in federated systems. Important challenges arise for machine learning algorithms in this scenario, such as reducing energy consumption and minimizing data exchange between devices in different zones. This paper introduces a novel method for accelerated training of parallel Support Vector Machines (pSVMs), based on ensembles, tailored to these kinds of problems. To achieve this, the training set is split into several Voronoi regions. These regions are small enough to permit faster parallel training of SVMs, reducing computational payload. Results from experiments comparing the proposed method with a single SVM and a standard ensemble of SVMs demonstrate that this approach can provide comparable performance while limiting the number of regions required to solve classification tasks. These advantages facilitate the development of energy-efficient policies in WSN.

scholarly journals BALANCED VS IMBALANCED TRAINING DATA: CLASSIFYING RAPIDEYE DATA WITH SUPPORT VECTOR MACHINES

2016 ◽  
Vol XLI-B7 ◽  
pp. 379-384 ◽  
Author(s):  
M. Ustuner ◽  
F. B. Sanli ◽  
S. Abdikan
Keyword(s):  
Support Vector Machines ◽  
Image Classification ◽  
Landscape Heterogeneity ◽  
Sample Selection ◽  
Training Data ◽  
Support Vector ◽  
Training Set ◽  
Learning Stage ◽  
Vector Machines ◽  
Imbalanced Training Data

The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size), resolution of input imagery and landscape heterogeneity. The design of training set is still a challenging issue since the sensitivity of classifier algorithm at learning stage is different for the same dataset. In this paper, the classification of RapidEye imagery with balanced and imbalanced training data for mapping the crop types was addressed. Classification with imbalanced training data may result in low accuracy in some scenarios. Support Vector Machines (SVM), Maximum Likelihood (ML) and Artificial Neural Network (ANN) classifications were implemented here to classify the data. For evaluating the influence of the balanced and imbalanced training data on image classification algorithms, three different training datasets were created. Two different balanced datasets which have 70 and 100 pixels for each class of interest and one imbalanced dataset in which each class has different number of pixels were used in classification stage. Results demonstrate that ML and NN classifications are affected by imbalanced training data in resulting a reduction in accuracy (from 90.94% to 85.94% for ML and from 91.56% to 88.44% for NN) while SVM is not affected significantly (from 94.38% to 94.69%) and slightly improved. Our results highlighted that SVM is proven to be a very robust, consistent and effective classifier as it can perform very well under balanced and imbalanced training data situations. Furthermore, the training stage should be precisely and carefully designed for the need of adopted classifier.

scholarly journals Support Vector Machines in HTS Data Mining: Type I MetAPs Inhibition Study

2005 ◽  
Vol 11 (2) ◽  
pp. 138-144 ◽  
Author(s):  
Jianwen Fang ◽  
Yinghua Dong ◽  
Gerald H. Lushington ◽  
Qi-Zhuang Ye ◽  
Gunda I. Georg
Keyword(s):  
Support Vector Machines ◽  
High Throughput Screening ◽  
Minimum Size ◽  
Support Vector ◽  
Type I ◽  
Training Set ◽  
Inhibition Study ◽  
Data Set ◽  
Test Set ◽  
Vector Machines

This article reports a successful application of support vector machines (SVMs) in mining high-throughput screening (HTS) data of a type I methionine aminopeptidases (MetAPs) inhibition study. A library with 43,736 small organic molecules was used in the study, and 1355 compounds in the library with 40% or higher inhibition activity were considered as active. The data set was randomly split into a training set and a test set (3:1 ratio). The authors were able to rank compounds in the test set using their decision values predicted by SVM models that were built on the training set. They defined a novel score PT50, the percentage of the test set needed to be screened to recover 50% of the actives, to measure the performance of the models. With carefully selected parameters, SVM models increased the hit rates significantly, and 50% of the active compounds could be recovered by screening just 7% of the test set. The authors found that the size of the training set played a significant role in the performance of the models. A training set with 10,000 member compounds is likely the minimum size required to build a model with reasonable predictive power.

scholarly journals BALANCED VS IMBALANCED TRAINING DATA: CLASSIFYING RAPIDEYE DATA WITH SUPPORT VECTOR MACHINES

2016 ◽  
Vol XLI-B7 ◽  
pp. 379-384
Author(s):  
M. Ustuner ◽  
F. B. Sanli ◽  
S. Abdikan
Keyword(s):  
Support Vector Machines ◽  
Image Classification ◽  
Landscape Heterogeneity ◽  
Sample Selection ◽  
Training Data ◽  
Support Vector ◽  
Training Set ◽  
Learning Stage ◽  
Vector Machines ◽  
Imbalanced Training Data

The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size), resolution of input imagery and landscape heterogeneity. The design of training set is still a challenging issue since the sensitivity of classifier algorithm at learning stage is different for the same dataset. In this paper, the classification of RapidEye imagery with balanced and imbalanced training data for mapping the crop types was addressed. Classification with imbalanced training data may result in low accuracy in some scenarios. Support Vector Machines (SVM), Maximum Likelihood (ML) and Artificial Neural Network (ANN) classifications were implemented here to classify the data. For evaluating the influence of the balanced and imbalanced training data on image classification algorithms, three different training datasets were created. Two different balanced datasets which have 70 and 100 pixels for each class of interest and one imbalanced dataset in which each class has different number of pixels were used in classification stage. Results demonstrate that ML and NN classifications are affected by imbalanced training data in resulting a reduction in accuracy (from 90.94% to 85.94% for ML and from 91.56% to 88.44% for NN) while SVM is not affected significantly (from 94.38% to 94.69%) and slightly improved. Our results highlighted that SVM is proven to be a very robust, consistent and effective classifier as it can perform very well under balanced and imbalanced training data situations. Furthermore, the training stage should be precisely and carefully designed for the need of adopted classifier.

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