Binary SVM Classifier Research Articles

Efficient 2D viewpoint combination for human action recognition

The ability to recognize human actions using a single viewpoint is affected by phenomena such as self-occlusions or occlusions by other objects. Incorporating multiple cameras can help overcome these issues. However, the question remains how to efficiently use information from all viewpoints to increase performance. Researchers have reconstructed a 3D model from multiple views to reduce dependency on viewpoint, but this 3D approach is often computationally expensive. Moreover, the quality of each view influences the overall model and the reconstruction is limited to volumes where the views overlap. In this paper, we propose a novel method to efficiently combine 2D data from different viewpoints. Spatio-temporal features are extracted from each viewpoint and then used in a bag-of-words framework to form histograms. Two different sizes of codebook are exploited. The similarity between the obtained histograms is represented via the Histogram Intersection kernel as well as the RBF kernel with $$\chi ^2$$?2 distance. Lastly, we combine all the basic kernels generated by selection of different viewpoints, feature types, codebook sizes and kernel types. The final kernel is a linear combination of basic kernels that are properly weighted based on an optimization process. For higher accuracy, the sets of kernel weights are computed separately for each binary SVM classifier. Our method not only combines the information from multiple viewpoints efficiently, but also improves the performance by mapping features into various kernel spaces. The efficiency of the proposed method is demonstrated by testing on two commonly used multi-view human action datasets. Moreover several experiments indicate the efficacy of each part of the method on the overall performance.

Face Recognition Across Gender Transformation Using SVM Classifier

The face recognition of medically altered faces under gender transformation is performed. Medical alteration is the gender transformation performed by undergoing hormone replacement therapy (HRT). Face images that are undergoing HRT based gender transformation over a period of several months to three years are taken. By altering the balance of sex hormones HRT achieves gender transformation. HRT causes changes in the physical appearance of the body and face. Changes in the face due to HRT affect the face recognition system. Existing system uses a face recognition system and the studies reveal that the Periocular region is invariant to the changes due to HRT. So a Periocular based face recognition system is developed. Initially perform face and eye detection then alignment and cropping is performed. Face images are represented using patch based local binary patterns. Face recognition is performed by using the binary classifier SVM Classifier.

A Novel and Principled Multiclass Support Vector Machine

Support vector machines SVMs have been demonstrated very efficient for binary classification problems; however, computationally efficient and effective multiclass SVMs are still missing. Most existing multiclass SVM classifiers are constructed either by combining multiple binary SVM classifiers, which often perform moderately for some problems, or by converting multiclass problems into one single optimization problem, which is unfortunately computationally expensive. To address these issues, a novel and principled multiclass SVM based on geometric properties of hyperspheres, termed SVMGH, is proposed in this paper. Different from existing SVM-based methods that seek a cutting hyperplane between two classes, SVMGH draws the discriminative information of each class by constructing a minimum hypersphere containing all class members, and then defines a label function based on the geometric properties of the minimum hyperspheres. We prove theoretically the geometric properties of the minimum hyperspheres to guarantee the validation of SVMGH. The computational efficiency is enhanced by a data reduction strategy as well as a fast training method. Experimental results demonstrate that the proposed SVMGH shows better performance and higher computational efficiency than the state of the art on multiclassification problems while maintaining comparable performance and efficiency on binary classification problems.

Improved diagnostics for the incipient faults in analog circuits using LSSVM based on PSO algorithm with Mahalanobis distance

Diagnostics of incipient faults for analog circuits is very important, yet very difficult. Traditionally, the soft faults can only be diagnosed under the occurrence of large variation of component parameters. In this paper, a novel approach for diagnosing incipient faults in analog circuits is proposed. First, a statistical property feature vector composed of range, mean, standard deviation, skewness, kurtosis, entropy and centroid is proposed. Then, the least squares support vector machine (LSSVM) is used for diagnostics of the incipient faults. Conventionally, multi-fault diagnosis for analog circuits based on SVM usually used a single feature vector to train all binary SVM classifier. However, in fact, each binary SVM classifier has different classification accuracy for different feature vectors. Thus, the particle swarm optimization (PSO) based on Mahalanobis distance (MD) is proposed to select a near-optimal feature vector for each binary classifier. The experimental results for three analog circuits show: (1) the accuracy using the near-optimal feature vectors is better than the accuracy using a single feature vector, and is also better than the accuracy using the optimal single feature vector; (2) the accuracy using the near-optimal feature vectors is close to the accuracy using the optimal feature vectors selected by the exhaustive method; (3) the accuracy using the near-optimal feature vectors based on LSSVM is better than the accuracy obtained by hidden Markov model; (4) the consuming time of the near-optimal feature vectors selected by MD is reduced by about 98% in comparison to the time of the optimal feature vectors.

Effective combining of color and texture descriptors for indoor-outdoor image classification

Although many indoor-outdoor image classification methods have been proposed in the literature, most of them have omitted comparison with basic methods to justify the need for complex feature extraction and classification procedures. In this paper we propose a relatively simple but highly accurate method for indoor-outdoor image classification, based on combination of carefully engineered MPEG-7 color and texture descriptors. In order to determine the optimal combination of descriptors in terms of fast extraction, compact representation and high accuracy, we conducted comprehensive empirical tests over several color and texture descriptors. The descriptors combination was used for training and testing of a binary SVM classifier. We have shown that the proper descriptors preprocessing before SVM classification has significant impact on the final result. Comprehensive experimental evaluation shows that the proposed method outperforms several more complex indoor-outdoor image classification techniques on a couple of public datasets.

Distinction between handwritten and machine-printed text based on the bag of visual words model

In a variety of documents, ranging from forms to archive documents and books with annotations, machine printed and handwritten text may coexist in the same document image, raising significant issues within the recognition pipeline. It is, therefore, necessary to separate the two types of text so that it becomes feasible to apply different recognition methodologies to each modality. In this paper, a new approach is proposed which strives towards identifying and separating handwritten from machine printed text using the Bag of Visual Words model (BoVW). Initially, blocks of interest are detected in the document image. For each block, a descriptor is calculated based on the BoVW. The final characterization of the blocks as Handwritten, Machine Printed or Noise is made by a decision scheme which relies upon the combination of binary SVM classifiers. The promising performance of the proposed approach is shown by using a consistent evaluation methodology which couples meaningful measures along with new datasets dedicated to the problem upon consideration.

TOPTMH: TOPOLOGY PREDICTOR FOR TRANSMEMBRANE α-HELICES

Alpha-helical transmembrane proteins mediate many key biological processes and represent 20%-30% of all genes in many organisms. Due to the difficulties in experimentally determining their high-resolution 3D structure, computational methods to predict the location and orientation of transmembrane helix segments using sequence information are essential. We present TOPTMH, a new transmembrane helix topology prediction method that combines support vector machines, hidden Markov models, and a widely used rule-based scheme. The contribution of this work is the development of a prediction approach that first uses a binary SVM classifier to predict the helix residues and then it employs a pair of HMM models that incorporate the SVM predictions and hydropathy-based features to identify the entire transmembrane helix segments by capturing the structural characteristics of these proteins. TOPTMH outperforms state-of-the-art prediction methods and achieves the best performance on an independent static benchmark.

Twin Support Vector Machines for Pattern Classification

We propose Twin SVM, a binary SVM classifier that determines two nonparallel planes by solving two related SVM-type problems, each of which is smaller than in a conventional SVM. The Twin SVM formulation is in the spirit of proximal SVMs via generalized eigenvalues. On several benchmark data sets, Twin SVM is not only fast, but shows good generalization. Twin SVM is also useful for automatically discovering two-dimensional projections of the data.