Intersection Kernel Support Vector Machine Research Articles

Recognition Method for Earthquake-induced Building Damage from Unmanned-aerial-vehicle-based Images Using Bag of Words and Histogram Intersection Kernel Support Vector Machine

Recognition Method for Earthquake-induced Building Damage from Unmanned-aerial-vehicle-based Images Using Bag of Words and Histogram Intersection Kernel Support Vector Machine

Motor Fault Diagnosis Using Image Visual Information and Bag of Words Model

In order to solve the tough issue that is difficult to extract the representative fault features of the hybrid vibration signals from the various industrial applications, a motor fault diagnosis method based on image visual information and bag of words model (BoW) are proposed in this paper. Based on the redundancy information of raw signals, the mapping which is between the original fault signal and the visual information is obtained by the methods of SDP (Symmetrized Dot Pattern) image and dense SIFT (Scale Invariant Feature Transform) features. The bag of visual word (BoVW) and pyramid histogram intersection kernel support vector machine (PHIK-SVM) are applying to complete the fault diagnosis and state recognition of related motors. The test results reveal that the diagnostic accuracy of four fault states could reach 99.50%, moreover, the diagnostic accuracy of different operating conditions under each fault could reach 91.83% as well. Compared with the traditional methods, the proposed method is able to obtain the detailed characteristics of various motors from the perspective of visual knowledge, furthermore, it is more robust and without complex data processing.

A Weighted Voting Classifiers Ensemble for the Brain Tumors Classification in MR Images

The appropriate treatment of cancers critically depends on the precise identification of the tumor types. The Magnetic Resonance Imaging (MRI) provides a non-invasive approach for detecting the tumors and classifying their types. Computer-aided diagnosis (CAD) systems using image analysis and machine learning algorithms assist radiologists in making error-free decisions. This paper presents an integrated approach for the segmentation and classification of three types of brain tumors (i.e. meningioma, glioma, and pituitary tumor) in real MRI images. First, the images quality is enhanced by removing the noises using wiener and median filters. Then, the support vector machine (SVM) classifier is performed to segment the MR image. Following this, 42 features are extracted, among which eleven most practical features are chosen by the Differential Evolution (DE). Using the selected features, tumor slices are categorized using the k-nearest neighbors (KNN), the weighted kernel width SVM (WSVM) and the histogram intersection kernel SVM (HIK-SVM) classifiers. Afterwards, these classifiers are combined using a multi-objective differential evolution (MODE)-based ensemble technique to attain the classification accuracy of 92.46%, evaluated using the five-fold cross validation method. The results of the proposed approach are also compared with the experienced radiologist ground truth which shows that the proposed MODE-based ensemble technique is able to achieve high classification scores, measured also by sensitivity and specificity indices.

Analysis of cosine distance features for speaker verification

In this paper, we describe a method for representing the acoustic similarity of a target speaker with respect to a set of known speakers as a feature for speaker verification. We propose a novel distance based representation by encoding the cosine distance between i-vectors of the utterances belonging to target speaker and reference speakers. The new feature is referred to as cosine distance feature (CDF) and is used with a support vector machine (SVM) classifier (CDF-SVM). We show that reference speakers who rank high in acoustic similarity to the target speaker are more important for better speaker discrimination. A sparse representation of the CDF, that retains only a few of the largest values which correspond to the most similar reference speakers in the CDF vector is found to perform better than the baseline CDF system. We also explore speaker specific CDF where each target speaker has specific subset of most acoustically similar reference speakers. We show that the acoustic similarities between the target and reference speakers are best captured using an intersection kernel SVM. Experimental results on the core short2-short3 condition of NIST 2008 SRE, for both female and male trials, show that the speaker specific CDF outperforms the i-vector and speaker independent CDF based state-of-the-art speaker verification systems.

A Low-Complexity Pedestrian Detection Framework for Smart Video Surveillance Systems

Pedestrian detection is a key problem in computer vision and is currently addressed with increasingly complex solutions involving compute-intensive features and classification schemes. In this scope, histogram of oriented gradients (HOG) in conjunction with linear support vector machine (SVM) classifier is considered to be the single most discriminative feature that has been adopted as a stand-alone detector as well as a key instrument in advance systems involving hybrid features and cascaded detectors. In this paper, we propose a pedestrian detection framework that is computationally less expensive as well as more accurate than HOG-linear SVM. The proposed scheme exploits the discriminating power of the locally significant gradients in building orientation histograms without involving complex floating point operations while computing the feature. The integer-only feature allows the use of powerful histogram inter-section kernel SVM classifier in a fast lookup-table-based implementation. Resultantly, the proposed framework achieves at least 3% more accurate detection results than HOG on standard data sets while being 1.8 and 2.6 times faster on conventional desktop PC and embedded ARM platforms, respectively, for a single scale pedestrian detection on VGA resolution video. In addition, hardware implementation on Altera Cyclone IV field-programmable gate array results in more than 40% savings in logic resources compared with its HOG-linear SVM competitor. Hence, the proposed feature and classification setup is shown to be a better candidate as the single most discriminative pedestrian detector than the currently accepted HOG-linear SVM.

Algorithmic optimisation of histogram intersection kernel support vector machine‐based pedestrian detection using low complexity features

Histogram intersection kernel support vector machine (SVM) is accepted as a better discriminator than its linear counterpart when used for pedestrian detection in images and video frames. Its computational complexity has, however, limited its use in practical real‐time detectors. To circumvent this problem, prior work proposed a low complexity detection framework based on integer‐only histograms of oriented gradient features which allow a look‐up table‐based implementation of kernel SVM leading to further simplification without compromising detection performance. This work describes several important enhancements made in the original framework related to the pre‐processing steps, feature calculation and training setup. Resultantly, the augmented framework, proposed in this study, stands out in terms of the detection accuracy and computational complexity compared to contemporary detectors. The best detector described in this study achieves 8 and 2% lesser miss rates (MRs) on ETH and INRIA pedestrian datasets, respectively, compared to the well‐known boosting cascades‐based aggregate channel feature detector despite avoiding complex floating point operations. Moreover, the proposed detector performs exceptionally better in scenarios where less than 10−2 false positives per image are desired as demonstrated through the MR versus false positive curves.

Pedestrian Detection Algorithm Combining HOG and SLBP

In order to solve the problem of pedestrian detection performance, the described operator was improved. In this paper, semantic local binary pattern (SLBP) and histogram of oriented gradient (HOG) are combined as new feature operator. This feature method would enrich the information and enhance the detection performance. And then histogram intersection kernel support vector machine (HIKSVM) classifier is trained by the augment feature. Because the time cost is too large by the conventional SVM. HIKSVM could make up this drawback, and significantly reduce the training time. The experiments on the INRIA pedestrian dataset show that the method obtained significant improvement in accuracy comparing to HOG descriptors.

Automatic visual inspection of a missing split pin in the China railway high-speed.

The split pin (SP) on the caliper brake is a vital component of the brake system of a bogie traveling along the China railway high-speed (CRH), and the absence of the SP could cause serious train accidents. A new automatic visual inspection method is proposed for the quick and accurate detection of SP faults of the CRH. The proposed approach is based on the histogram of gradient (HOG) combined with the complete local binary pattern (CLBP). First, a fast pyramid template matching technique is presented for localizing the region of interest to reduce the searching scope. Under the multiresolution pyramid model for target localization, a coarse-to-fine strategy is employed to ensure that the recognizing speed of the SP for the entire image is increased significantly. Second, a hierarchical framework is adopted at the localizing and inspecting stages of the SP to automatically implement the inspection tasks. To increase the robustness to the outside complex illumination, the HOG feature for localizing the target and the CLBP feature for examining the state of the SP (i.e., missing or not-missing) are extracted in the Sobel gradient domain. The localization and recognition stages are both fulfilled through the use of their respective intersection kernel support vector machine classifiers and corresponding features. In conclusion, experimental results indicate that the inspection system achieves a high accuracy rate of more than 99.0% and a real-time speed, thus proving that the proposed method is effective for the fault inspection of the SP and can satisfy the requirements of CRH's actual application.

Generalizing intersection kernel support vector machines for color texture based recognition

This paper presents a novel recognition approach in which the component-adaptive color co-occurrence matrices (CACCMs) are designed to characterize color and texture cues in the images, while histogram intersection kernel support vector machines (HIKSVMs) are generalized to the version compatible to color co-occurrence matrix (CCM), called CCM intersection kernel support vector machines (CIKSVMs). An ensemble learning framework is proposed for synchronously training the optimal marginal CIKSVMs and corresponding CACCMs’ extractors. This learning architecture is applicable to an arbitrary color space employed for image coding, while we pay utmost attention to a perceptual uniform color space for the prominent potential in image proprieties’ display. For the formulation of recognition algorithm, the set of multi-channel CACCMs (CAMCMs) of per sample is utilized to get a balance between discriminative power and computational efficiency, while multiple marginal CIKSVMs are combined by weighted majority voting. The effectiveness of our approach is validated by promising results obtained from four experimental datasets.

Recognition of driver’s fatigue expression using Local Multiresolution Derivative Pattern

To develop the human-centric driver fatigue monitoring system for automatic understanding and charactering of driver's conditions, a novel, efficient feature extraction approach, named Local Multiresolution Derivative Pattern (LMDP), is proposed to describe the driver's fatigue expression images, and the Intersection Kernel Support Vector Machines classifier is then exploited to recognize three pre-defined classes of fatigue expressions, i.e., awake expressions, moderate fatigue expressions and severe fatigue expressions. With features extracted from a fatigue expressions dataset created at Southeast University, the holdout and cross-validation experiments on fatigue expressions classification are conducted by the Intersection Kernel Support Vector Machines classifier, compared with three commonly used classification methods including the k-nearest neighbor classifier, the multilayer perception classifier and the dissimilarity-based classifier. The experimental results of holdout and cross-validation showed that LMDP offers the better performance than Local Derivative Pattern, and the second order LMDP exceeds other order LMDP. With the second order LMDP and the Intersection Kernel Support Vector Machines classifier, the classification accuracies of the severe fatigue are over 90 in the holdout and cross-validation experiments, thus demonstrating the effectiveness of the proposed feature extraction method in automatically understanding the driver's conditions towards the human-centric driver fatigue monitoring system.

Fast pedestrian detection based on feature of local model

This paper studies pedestrian detection algorithm based on trapezoidal feature of local model, and applied to his- togram intersection kernel support vector machine (HIKSVM) for verification. In comparison with traditional asymmetry, rectangle and triangle features, the experimental results from trapezoid feature of local model indicate that it can more effec- tively describe the pedestrian's postures and promote the accuracy and robustness of pedestrian detection. Experimental results show that the proposed algorithm is robust and accurate against cluttered dynamical background, occlusion and the object deformation, and tested in many pedestrian datasets and achieved good results.

Image Classification Based on Histogram Intersection Kernel

Histogram Intersection Kernel Support Vector Machines (SVM) was used for the image classification problem. Specifically, each image was split into blocks, and each block was represented by the Scale Invariant Feature Transform (SIFT) descriptors; secondly, k-means cluster method was applied to separate the SIFT descriptors into groups, each group represented a visual keywords; thirdly, count the number of the SIFT descriptors in each image, and histogram of each image should be constructed; finally, Histogram Intersection Kernel should be built based on these histograms. In our experimental study, we use Corel-low images to test our method. Compared with typical RBF kernel SVM, the Histogram Intersection kernel SVM performs better than RBF kernel SVM.

An Enhanced Histogram of Oriented Gradients for Pedestrian Detection

The outstanding Histogram-of-Oriented-Gradients (HOG) feature proposed by Dalal and Triggs is a state-of-art technique for pedestrian detection, and it is usually applied with a linear support vector machine (SVM) in a sliding-window framework. Most other algorithms for pedestrian detection use HOG as the basic feature, and combine other features with HOG to form the feature set. Hence, the HOG feature is actually the most efficient and fundamental feature for pedestrian detection. However, the HOG feature cannot adequately handle scale variation of pedestrians. In addition, simply downsampling an image into a different scale, or decomposing via wavelet into multi-resolution subimages, calculating their HOG feature and combining them cannot enhance performance. Therefore, in this paper, based on the idea of multi-resolution feature descriptors, we propose a new robust edge feature referred to as Enhanced HOG (eHOG). It is a complementary descriptor for the histograms-of-oriented-gradients feature. Though the extraction process of the eHOG descriptor is derived only from HOG itself, similar to the process of extracting edge information from the downscaling image, it retains much more information for the edge gradient than that of the original HOG, without significantly increasing the complexity of computation. In the INRIA pedestrian dataset, many experiments have been conducted with eHOG and HOG, and the results show that the proposed new feature consistently improves the detection rate more than the original HOG feature detector. Particularly, eHOG with a Histogram Intersection Kernel SVM (HIKSVM) classifier has greatly improved performance. These results suggest that eHOG may be a better substitute for HOG for pedestrian detection in many applications.

Recognizing driving postures by combined features of contourlet transform and edge orientation histogram, and random subspace classifier ensembles

In order to develop Human-centered Driver Assistance Systems (HDAS), an efficient Combined Feature (CF) extraction approach from Contourlet Transform (CT) and Edge Orientation Histogram (EOH) is proposed for vehicle driving posture descriptions. A Random Subspace Ensemble (RSE) of Intersection Kernel Support Vector Machines (IKSVMs) is then exploited as the base classifier. Four testing driving postures are grasping the steering wheel, operating the shift lever, eating a cake, and talking on a cellar phone. On a dedicated Southeast University Driving Posture (SEU-DP) Database, the holdout and cross-validation experiments were conducted. The experimental results show that the proposed CF-RSE approach outperforms single Contourlet-IKSVM, EOH-IKSVM recognition strategies. With CF-RSE, the average classification accuracies of four driving posture classes are over 90%. Among the four classes of driving postures, the class of grasping the steering wheel is the most difficult to recognize and the proposed approach achieved over 85% accuracy in both experiments. These encouraging results show that the proposed CF-RSE approach is effective and hence has great promises in developing a successful HDAS.

Support vector machines with piecewise linear feature mapping

As the simplest extension to linear classifiers, piecewise linear (PWL) classifiers have attracted a lot of attention, because of their simplicity and classification capability. In this paper, a PWL feature mapping is introduced by investigating the property of the PWL classification boundary. Then support vector machines (SVM) with PWL feature mappings are proposed, called PWL-SVMs. In this paper, it is shown that some widely used PWL classifiers, such as k-nearest-neighbor, adaptive boosting of linear classifier and intersection kernel support vector machine, can be represented by the proposed feature mapping. That means the proposed PWL-SVMs at least can achieve the performance of the above PWL classifiers. Moreover, PWL-SVMs enjoy good properties of SVM and the performance on numerical experiments illustrates the effectiveness. Then some extensions are discussed and the application of PWL-SVMs can be expected.

Recognition of driving postures by multiwavelet transform and multilayer perceptron classifier

To develop Human-centric Driver Assistance Systems (HDAS) for automatic understanding and charactering of driver behaviors, an efficient feature extraction of driving postures based on Geronimo–Hardin–Massopust (GHM) multiwavelet transform is proposed, and Multilayer Perceptron (MLP) classifiers with three layers are then exploited in order to recognize four pre-defined classes of driving postures. With features extracted from a driving posture dataset created at Southeast University (SEU), the holdout and cross-validation experiments on driving posture classification are conducted by MLP classifiers, compared with the Intersection Kernel Support Vector Machines (IKSVMs), the k-Nearest Neighbor (kNN) classifier and the Parzen classifier. The experimental results show that feature extraction based on GHM multwavelet transform and MLP classifier, using softmax activation function in the output layer and hyperbolic tangent activation function in the hidden layer, offer the best classification performance compared to IKSVMs, kNN and Parzen classifiers. The experimental results also show that talking on a cellular phone is the most difficult one to classify among four predefined classes, which are 83.01% and 84.04% in the holdout and cross-validation experiments respectively. These results show the effectiveness of the feature extraction approach using GHM multiwavelet transform and MLP classifier in automatically understanding and characterizing driver behaviors towards Human-centric Driver Assistance Systems (HDAS).