Mahalanobis Distance Classifier Research Articles

고해상도 수치항공정사영상기반 하천토지피복지도 제작을 위한 분류기법 연구

The information on the land cover along stream corridor is important for stream restoration and maintenance activities. This study aims to review the different classification methods for mapping the status of stream corridors in Seom River using airborne RGB and CIR digital ortho imagery with a ground pixel resolution of 0.2m. The maximum likelihood classification, minimum distance classification, parallelepiped classification, mahalanobis distance classification algorithms were performed with regard to the improvement methods, the skewed data for training classifiers and filtering technique. From these results follows that, in aerial image classification, Maximum likelihood classification gave results the highest classification accuracy and the CIR image showed comparatively high precision.

Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image

Powdery mildew, caused by the fungus Blumeria graminis, is a major winter wheat disease in China. Accurate delineation of powdery mildew infestations is necessary for site-specific disease management. In this study, high-resolution multispectral imagery of a 25 km2 typical outbreak site in Shaanxi, China, taken by a newly-launched satellite, SPOT-6, was analyzed for mapping powdery mildew disease. Two regions with high representation were selected for conducting a field survey of powdery mildew. Three supervised classification methods—artificial neural network, mahalanobis distance, and maximum likelihood classifier—were implemented and compared for their performance on disease detection. The accuracy assessment showed that the ANN has the highest overall accuracy of 89%, following by MD and MLC with overall accuracies of 84% and 79%, respectively. These results indicated that the high-resolution multispectral imagery with proper classification techniques incorporated with the field investigation can be a useful tool for mapping powdery mildew in winter wheat.

A Self‐learning Framework for Statistical Ground Classification using Radar and Monocular Vision

Reliable terrain analysis is a key requirement for a mobile robot to operate safely in challenging environments, such as in natural outdoor settings. In these contexts, conventional navigation systems that assume a priori knowledge of the terrain geometric properties, appearance properties, or both, would most likely fail, due to the high variability of the terrain characteristics and environmental conditions. In this paper, a self‐learning framework for ground detection and classification is introduced, where the terrain model is automatically initialized at the beginning of the vehicle's operation and progressively updated online. The proposed approach is of general applicability for a robot's perception purposes, and it can be implemented using a single sensor or combining different sensor modalities. In the context of this paper, two ground classification modules are presented: one based on radar data, and one based on monocular vision and supervised by the radar classifier. Both of them rely on online learning strategies to build a statistical feature‐based model of the ground, and both implement a Mahalanobis distance classification approach for ground segmentation in their respective fields of view. In detail, the radar classifier analyzes radar observations to obtain an estimate of the ground surface location based on a set of radar features. The output of the radar classifier serves as well to provide training labels to the visual classification module. Once trained, the vision‐based classifier is able to discriminate between ground and nonground regions in the entire field of view of the camera. It can also detect multiple terrain components within the broad ground class. Experimental results, obtained with an unmanned ground vehicle operating in a rural environment, are presented to validate the system. It is shown that the proposed approach is effective in detecting drivable surface, reaching an average classification accuracy of about 80% on the entire video frame with the additional advantage of not requiring human intervention for training or a priori assumption on the ground appearance.

BCRgt: a Bayesian cluster regression-based genotyping algorithm for the samples with copy number alterations

BackgroundAccurate genotype calling is a pre-requisite of a successful Genome-Wide Association Study (GWAS). Although most genotyping algorithms can achieve an accuracy rate greater than 99% for genotyping DNA samples without copy number alterations (CNAs), almost all of these algorithms are not designed for genotyping tumor samples that are known to have large regions of CNAs.ResultsThis study aims to develop a statistical method that can accurately genotype tumor samples with CNAs. The proposed method adds a Bayesian layer to a cluster regression model and is termed a Bayesian Cluster Regression-based genotyping algorithm (BCRgt). We demonstrate that high concordance rates with HapMap calls can be achieved without using reference/training samples, when CNAs do not exist. By adding a training step, we have obtained higher genotyping concordance rates, without requiring large sample sizes. When CNAs exist in the samples, accuracy can be dramatically improved in regions with DNA copy loss and slightly improved in regions with copy number gain, comparing with the Bayesian Robust Linear Model with Mahalanobis distance classifier (BRLMM).ConclusionsIn conclusion, we have demonstrated that BCRgt can provide accurate genotyping calls for tumor samples with CNAs.

Classification of in-tube boiling R134a data belonging to the smooth and corrugated tubes

Different types of tubes have been used to improve cooling systems regarding the performance, cost and compactness. Corrugated tubes are tubes with their inner surface enhanced in these systems. One of the applications of machine learning, named as pattern classification, is often used to separate the human faces, voices, finger prints etc. In this study, it is used to separate the R134a data taken in-tube boiling process in smooth and enhanced tubes automatically. In other words, the developed numerical algorithms enabled artificial intelligence to predict the type of tubes having equivalent diameters used in the experiments. Systematical experiments, including saturation temperatures of 10, 15 and 20°C, mass fluxes of 200, 300 and 400kgm−2s−1 and heat fluxes of 20, 25 and 30Wm−2, are carried out for the comparisons using smooth and 5 different types of corrugated tubes having various corrugation depths and helix angles. The boiling process in the test tubes has been measured with 300 data points, having 30 individual parameters (inputs) for varying tube types for the reduction process by the Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA). The classification success rates of the methods of Linear Discriminant Classifier (LDC), Quadratic Discriminant Classifier (QDC), Naive Bayes Classifier (NBC) and Minimum Mahalanobis Distance Classifier (MMDC) by each dimensional reduction and the total area occupying under receiver operating characteristic (ROC) curves are determined according to 3-fold cross validation method. NBC method has the highest classification success with the accuracy of 98.33% as a result of the reduction to 3 dimensions by LDA method. In addition to this, QDC method has the highest area under curve (AUC) with the value of 0.9994 according to the reduction to 3 dimensions by LDA method. Dependency analyses showed that the use of 8 dimensional experimental parameters as the most important input is enough to determine the type of test tubes with a high accuracy.

A Comparative Study on Classification Methods of Sleep Stages by Using EEG

Electrophysiological recordings are considered a reliable method of assessing a person’s alertness. Sleep medicine is asked to offer objective methods to measure daytime alertness, tiredness and sleepiness. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in recognition of alertness level. In this paper, EEG signals have been analyzed using wavelet transform and classification using statistical classifiers such as euclidean and mahalanobis distance classifiers and a promising method SVM (Support Vector Machine). As a result of simulation, the average values of accuracies for the Linear Discriminant Analysis (LDA)-Quadratic, k-Nearest Neighbors (k-NN)-Euclidean, and Linear SVM were 48%, 34.2%, and 86%, respectively. The experimental results show that SVM classification method offer the better performance for reliable classification of the EEG signal in comparison with the other classification methods.

Using phenological metrics and the multiple classifier fusion method to map land cover types

Feature selection and multiple classifier fusion (MCF) are effective approaches to improve land cover classification accuracy. In this study, we combined phenological metrics and the MCF method to map land cover types in Jiangsu province of China during the second crop growing season using moderate resolution imaging spectroradiometer time-series data. Eight phenological metrics were developed and calculated, and a MCF scheme was proposed by combining a simple majority vote and the measurement of posterior probabilities. The four base classifiers (i.e., the maximum likelihood classifier, the Mahalanobis distance classifier, the support vector machine classifier, and the neural networks classifier) and the MCF method were used in classifications using two spectral indices from the original satellite data (direct classification) and the computed metric data (metrics-based classification). Accuracy assessments indicated that the overall accuracies and kappa coefficients of the metrics-based classifications were all higher than those of direct classifications. The average overall accuracy and kappa coefficient of metrics-based classifications were 8.36% and 0.1 higher than that of direct classifications, respectively. Similarly, the overall accuracy and kappa coefficient of MCF generally were close to or exceeded the highest accuracy among all the base classifiers. The highest overall accuracy and kappa coefficient was achieved by classification with the MCF method based on phenological metrics (m-MCF), which were 88% and 0.85, respectively. Our results suggested that combining phenological metrics and MCF in classification is a promising method for land cover mapping in regions where strong phenological signals can be detected.

Human Detection by Quadratic Classification on Subspace of Extended Histogram of Gradients

This paper proposes a quadratic classification approach on the subspace of Extended Histogram of Gradients (ExHoG) for human detection. By investigating the limitations of Histogram of Gradients (HG) and Histogram of Oriented Gradients (HOG), ExHoG is proposed as a new feature for human detection. ExHoG alleviates the problem of discrimination between a dark object against a bright background and vice versa inherent in HG. It also resolves an issue of HOG whereby gradients of opposite directions in the same cell are mapped into the same histogram bin. We reduce the dimensionality of ExHoG using Asymmetric Principal Component Analysis (APCA) for improved quadratic classification. APCA also addresses the asymmetry issue in training sets of human detection where there are much fewer human samples than non-human samples. Our proposed approach is tested on three established benchmarking data sets--INRIA, Caltech, and Daimler--using a modified Minimum Mahalanobis distance classifier. Results indicate that the proposed approach outperforms current state-of-the-art human detection methods.

An Entropy-Based Multispectral Image Classification Algorithm

Employing the entropy theory, this paper presents a new and robust multispectral image classification algorithm. The digital number (DN) in remotely sensed multispectral images is considered as a random variable when judging the allocation of unknown pixels into predefined training classes. If an unknown pixel shows a similar DN vector as the pixels in a training class, it will increase the global entropy defined as the sum of DN probabilities multiplied by the logarithm of DN probabilities for all pixels within the training class. The unknown pixel is to be assigned to the class for which the entropy of the training class is increased most due to the inclusion of the pixel. The proposed entropy-based classification (EC) is compared with the maximum likelihood classification (MLC), parallelepiped classification, minimum distance classification, Mahalanobis distance classification (MDC), iterative self-organizing data analysis technique (ISODATA) classification, and K-means classification. These classifiers were applied to a Landsat Enhanced Thematic Mapper Plus image covering Houston, Texas, USA, acquired on October 16, 1999. A reference land cover map from the National Land Cover Data 2001 of the same area was taken as a ground reference to assess the accuracy of classification results, suggesting that the EC showed comparable overall accuracy as MDC, and they both outperformed other classifiers. The results of MLC can be improved by substituting the multivariate lognormal or gamma distribution for the multivariate normal distribution involved in its assumption. The EC algorithm has the potential to produce reliable land cover maps regardless of the distribution of DN vectors and relevant parameters of probability density functions involved in other classifiers.

Automatic Fungal Disease Detection based on Wavelet Feature Extraction and PCA Analysis in Commercial Crops

This paper describes automatic detection and classification of v isual symptoms affected by fungal disease. Algorithms are developed to acquire and process color images of fungal disease affected on commercial crops like chili, cotton and sugarcane. The developed algorith ms are used to preprocess, segment, extract and reduce features fro m fungal affected parts of a crop. The feature extract ion is done with discrete wavelet transform (DWT) and features are further reduced by using Principal co mponent analysis (PCA). Reduced features are then used as inputs to classifiers and tests are performed to classify image samples. We have used statistical based Mahalanobis distance and Probabilistic neural network (PNN) classifiers. The average classification accuracies using Mahalanobis distance classifier are 83.17% and using PNN classifier are 86.48%.

Hyperspectral Imaging-Based Classification and Wavebands Selection for Internal Defect Detection of Pickling Cucumbers

Hyperspectral imaging is useful for detecting internal defects of pickling cucumbers. The technique, however, is not yet suitable for high-speed online implementation due to the challenges in analyzing large-scale hyperspectral images. This research aimed to select the optimal wavebands from the hyperspectral image data, so that they can be deployed in either a hyperspectral or multispectral imaging-based inspection system for the automatic detection of internal defects of pickling cucumbers. Hyperspectral reflectance (400–700 nm) and transmittance (700–1,000 nm) images were acquired, using an in-house developed hyperspectral imaging system running at two conveyor speeds of 85 and 165 mm/s, for 300 “Journey” pickling cucumbers before and after internal damage was induced by mechanical load. Minimum redundancy–maximum relevance (MRMR) was used for optimal wavebands selection, and the loadings of principal component analysis (PCA) were also applied for qualitatively identifying the important wavebands that are related to the specific features. Discriminant analysis with Mahalanobis distance classifier was performed for the two-class (i.e., normal and defective) and three-class (i.e., normal, slightly defective, and severely defective) classifications using the mean spectra and textural features (energy and variance) from the regions of interest in the spectral images at selected waveband ratios. The classification results based on MRMR wavebands selection were generally better than those from PCA-based classifications. The two-band ratio of 887/837 nm from MRMR gave the best overall classification results, with the accuracy of 95.1 and 94.2 % at the conveyor speeds of 85 and 165 mm/s, respectively, for the two-class classification. The highest classification accuracies for the three-class classification based on the optimal two-band ratio of 887/837 nm were 82.8 and 81.3 % at the conveyor speeds of 85 and 165 mm/s, respectively. The mean spectra-based classification achieved better results than the textural feature-based classification, except in the three-class classification for the higher conveyor speed. The overall classification accuracies for all selected waveband ratios at the low conveyor speed were slightly higher than those at the higher conveyor speed, since the low speed resulted in more scan lines, thus higher spatial resolution hyperspectral images. The identified two-band ratio of 887/837 nm in transmittance mode could be applied for fast real-time internal defect detection of pickling cucumbers.

Advanced Classification of Lands at TM and Envisat Images of Mongolia

The aim of this study is to fuse high resolution optical and microwave images and classify urban land cover types using a refined Mahalanobis distance classifier. For the data fusion, multiplicative method, Brovey transform, intensity-huesaturation method and principal component analysis are used and the results are compared. The refined method uses spatial thresholds defined from local knowledge and the bands defined from multiple sources. The result of the refined Mahalanobis distance method is compared with the result of a standard technique and it demonstrates a higher accuracy. Overall, the research indicates that the combined use of optical and microwave images can notably improve the interpretation and classification of land cover types and the refined Mahalanobis classification is a powerful tool to increase classification accuracy.

Fall detection using single-tree complex wavelet transform

The goal of Ambient Assisted Living (AAL) research is to improve the quality of life of the elderly and handicapped people and help them maintain an independent lifestyle with the use of sensors, signal processing and telecommunications infrastructure. Unusual human activity detection such as fall detection has important applications. In this paper, a fall detection algorithm for a low cost AAL system using vibration and passive infrared (PIR) sensors is proposed. The single-tree complex wavelet transform (ST-CWT) is used for feature extraction from vibration sensor signal. The proposed feature extraction scheme is compared to discrete Fourier transform and mel-frequency cepstrum coefficients based feature extraction methods. Vibration signal features are classified into “fall” and “ordinary activity” classes using Euclidean distance, Mahalanobis distance, and support vector machine (SVM) classifiers, and they are compared to each other. The PIR sensor is used for the detection of a moving person in a region of interest. The proposed system works in real-time on a standard personal computer.

Impact of Urban Land-Cover Classification on Groundwater Recharge Uncertainty

Objective and detailed mapping of urban land-cover types over large areas is important for hydrological modelling, as most man-made land-cover consist of sealed surfaces which strongly reduce groundwater recharge. Moreover, impervious surfaces are the predominant type in urbanized areas and can lead to increased surface runoff. Classification of man-made objects in urbanized areas is not straightforward due to similarity in spectral properties. This study examines the use of hyperspectral CHRIS-Proba images for complex urban land-cover classification of the Woluwe River catchment, Brussels, Belgium. Two methods are compared: 1) a multiscale region-based classification approach, which is based on a causal Markovian model being defined on a Multiscale Region Adjacency Tree and a set of nonparametric dissimilarity measures; and 2) a pixel based classification method with a Mahalanobis distance classifier. Multiscale region-based classification results in a Kappa value of 0.95 while pixel-based classification has a slightly lower Kappa value of 0.92. The impact of the classification method on the hydrology is estimated with the application of the WetSpass physically-based distributed water balance model. The model uncertainty is assessed with the use of a Monte Carlo simulation. Model results show that the region-based classification yields to a higher yearly recharge than the pixel-based classification. The overall uncertainty, quantified by the Monte Carlo method is lower for the region-based classification than for the pixel-based classification. The presented study indicates that the selection of the classification technique is of critical importance for the outcome of hydrological models.

Evaluation of LISS-III Sensor Data of IRS-P6 Satellite for Detection Saline Soils (Case Study: Najmabad Region)

Soil Salinity has been a large problem in arid and semi arid regions. Preparation of such maps is useful for Natural resource managers. Old methods of preparing such maps require a lot of time and cost. Multi-spectral remotely sensed dates due to the broad vision and repeating of these imageries is suitable for provide saline soil maps. This investigation is conducted to provide saline soil maps with sensor LISS-III of IRS-P6 satellite data, in Najmabad of Savojbolagh. Satellite images belonging to 25 June 2006. For enhancement of images, salt Indices, Digital Elevation Model (DEM), False Color Composite imageries (FCC) and Principal Component Analysis (PCA), were used. Supervised classification method includes Box classifier, Minimum Distance, Minimum Mahalanobis Distance and Maximum Likelihood classifier, DEM, PCA1, PCA4 and Saline Indices (SI) were used. After classification, the class map salinity S0, S1, S2, S3 S4, were prepared. The results shows highest overall accuracy and kappa coefficient for the maximum Likelihood classifier estimate, respectively 99% and 97% and the lowest overall accuracy and kappa coefficient for PCA1 estimate, respectively 1% and 0% were obtained. Using Digital Elevation Model (DEM) also due to the difference in height position to the separation of saline lands is usefully. Most spectral interference relatedto non-saline soils and low saline soil. From among indices INT2 and PVI greatest ability to segregate is salty soils(especially classes S0 and S1).

Anomaly Detection and Classification for PHM of Electronics Subjected to Shock and Vibration

Failures in electronics subjected to shock and vibration are typically diagnosed using the built-in self test (BIST) or using continuity monitoring of daisy-chained packages. The BIST, which is extensively used for diagnostics or identification of failure, is focused on reactive failure detection and provides limited insight into reliability and residual life. In this paper, a new technique has been developed for health monitoring and failure mode classification based on measured damage precursors. A feature extraction technique in the joint-time-frequency analysis (JTFA) domain has been developed along with pattern classifiers for fault diagnosis of electronics at the product level. The Karhunen Loéve transform (KLT) has been used for feature reduction and de-correlation of the feature vectors for fault-mode classification in electronic assemblies. Euclidean, and Mahalanobis, and Bayesian distance classifiers based on JTFA, have been used for classification of the resulting feature space. Previously, the authors have developed damage precursors based on time and spectral techniques for health monitoring of electronics without reliance on continuity data from daisy-chained packages. Statistical pattern recognition techniques based on wavelet packet energy decomposition have been studied by authors for quantification of shock damage in electronic assemblies and auto-regressive moving average; time-frequency techniques have been investigated for system identification, condition monitoring, and fault detection and diagnosis in electronic systems. However, identification of specific failure modes is not possible. In this paper, various fault modes, such as solder interconnect failure, interconnect missing, chip delamination, chip cracking etc., in various packaging architectures have been classified using clustering of feature vectors based on the KLT approach. The KLT de-correlates the feature space and identifies dominant directions to describe the space, eliminating directions that encode little useful information about the features. The clustered damage precursors have been correlated with underlying damage. Several chip-scale packages have been studied with lead-free second-level interconnects, including SAC105, SAC305 alloys. Transient strain has been measured during the drop event using digital image correlation and high-speed cameras operating at 100 000 frames/s. Continuity has been monitored simultaneously for failure identification. Fault-mode classification has been done using KLT and JTFA analysis of the experimental data. In addition, explicit finite element models have been developed, and various kinds of failure modes have been simulated, such as solder ball cracking, trace fracture, package falloff, and solder ball failure. Models using cohesive elements present at the solder joint-copper pad interface at both the printed circuit board and package side have also been created to study the traction-separation behavior of solder. Fault modes predicted by simulation-based precursors have been correlated with those from experimental data.

Developing an unsupervised classification algorithm for characterization of steel properties

Purpose – The purpose of this paper is to develop an unsupervised classification algorithm including feature selection for industrial product classification with the basic philosophy of a supervised Mahalanobis‐Taguchi System (MTS).Design/methodology/approach – Two novel unsupervised classification algorithms called Unsupervised Mahalanobis Distance Classifier (UNMDC) are developed based on Mahalanobis' distance for identifying “abnormals” as individuals (or, groups) including feature selection. The identification of “abnormals” is based on the concept of threshold value in MTS and the distribution property of Mahalanobis‐D2.Findings – The performance of this algorithm, in terms of its efficiency and effectiveness, has been studied thoroughly for three different types of steel product on the basis of its composition and processing parameters. Performance in future diagnosis on the basis of useful features by the new scheme is found quite satisfactory.Research limitations/implications – This new algorithm ...

Classification of oil palm fresh fruit bunches based on their maturity using portable four-band sensor system

Classification of oil palm fresh fruit bunch (FFB) maturity is a critical factor that dictates the quality of produced palm oil. This study evaluates a multi-band portable, active optical sensor system; comprising of four spectral bands, 570, 670, 750, and 870nm, to detect oil palm FFB maturity. The in-field spectral reflectance data were collected using the sensor system from a total of 120 fresh fruit bunches. These fruit bunches were categories into unripe, ripe, and overripe classes. Different classifiers were applied to assess the applicability of using the sensor system. Based on the classification accuracies, data analysis on the spectral features (reflectance data and other features extracted from vegetation indices) indicated that the spectral reflectance data could be valuable in predicting the maturity of the fruit bunches. The quadratic discriminant analysis and discriminant analysis with Mahalanobis distance classifiers yielded highest average overall accuracies of greater than 85% in classifying oil palm FFB maturity. Additionally, the average individual class (unripe, ripe, and overripe) classification accuracies were also higher than 80%. Thus, optical sensing using four-band sensor system could be useful for oil palm FFB maturity classification under field condition.

The Effect of Regions of Interest and Spectral Pre-Processing on the Detection of Non-0157 Shiga-Toxin Producing Escherichia Coli Serogroups on Agar Media by Hyperspectral Imaging

Foodborne infection caused by Shiga toxin-producing Escherichia coli (STEC) is a major worldwide health concern. The best known and highly virulent STEC serogroup is E. coli 0157:H7, which can be easily identified when cultured on sorbitol-MacConkey (SMAC) agar. Recently, six non-0157 STEC serogroups have been found to cause human illnesses. These non-0157 serogroups ferment sorbital and form pink colonies; therefore SMAC agar cannot be used to differentiate non-0157 serogroups from each other and other flora growing on the plate. This study investigated the potential of visible and near infrared hyperspectral imaging and chemometrics to spectrally differentiate six representative non-0517 STEC serogroups (026, 045, 0103, 0111, 0121 and 0145) grown as spots on Rainbow agar media. Mahalanobis distance classifiers were developed with spectra obtained from ground truth regions of interest (ROIs) of each serogroup colony. The ROIs were selected as a doughnut-like open-ellipse to only include the leading edge of growth and as a closed-ellipse covering the entire colony. For each ROI type, the Mahalanobis distance classifiers were developed with log (1/Reflectance), first derivative and standard normal variate and detrending (SNVD) pre-processing treatments. Serogroups 045 and 0121 were consistently classified over 98% accurate, regard less of the classification algorithm used. The lowest classification accuracies were from classifiers developed with only log (1/ R) ROI spectra. First derivative and SNVD spectra helped to increase the detection accuracies of the other serogroups. The classification accuracy for serogroups 026, 0111, 0103 and 0145 with the closed-ellipse and open-ellipse classification algorithms showed varying results from 8% to 87% and 57% to 100%, respectively. The lower accuracies with closed ellipse spectra were due to greater spectral variation in the centre pixels on a per-pixel basis. Practical implications of this study are the demonstrated potential of hyperspectral imaging for presumptive-positive screening of non-0157 serogroups on Rainbow agar and the extensibility of the developed sampling methods and classification models for future research to identify the target bacteria in the presence of background flora grown on spread plates.

A NEW GENOTYPE CALLING METHOD FOR AFFYMETRIX SNP ARRAYS

Current genotype-calling methods such as Robust Linear Model with Mahalanobis Distance Classifier (RLMM) and Corrected Robust Linear Model with Maximum Likelihood Classification (CRLMM) provide accurate calling results for Affymetrix Single Nucleotide Polymorphisms (SNP) chips. However, these methods are computationally expensive as they employ preprocess procedures, including chip data normalization and other sophisticated statistical techniques. In the small sample case the accuracy rate may drop significantly. We develop a new genotype calling method for Affymetrix 100 k and 500 k SNP chips. A two-stage classification scheme is proposed to obtain a fast genotype calling algorithm. The first stage uses unsupervised classification to quickly discriminate genotypes with high accuracy for the majority of the SNPs. And the second stage employs a supervised classification method to incorporate allele frequency information either from the HapMap data or from a self-training scheme. Confidence score is provided for every genotype call. The overall performance is shown to be comparable to that of CRLMM as verified by the known gold standard HapMap data and is superior in small sample cases. The new algorithm is computationally simple and standalone in the sense that a self-training scheme can be used without employing any other training data. A package implementing the calling algorithm is freely available at http://www.sfs.ecnu.edu.cn/teachers/xuj_en.html.