Minimum Distance Classifier Research Articles

A method of feature selection in the aspect of specific identification of radar signals

Abstract This article presents an important task of classification, i.e. mapping surfaces which separate patterns in feature space in the scope of radar emitter recognition (RER) and classification. Assigning a tested radar to a particular class is based on defining its location from the discriminating areas. In order to carry out the classification process, it is necessary to define metrics in the feature space as it is essential to estimate the distance of a classified radar from the centre of the class. The method presented in this article is based on extraction and selection of distinctive features, which can be received in the process of specific emitter identification (SEI) of radar signals, and on the minimum distance classification. The author suggests a RER system which consists of a few independent channels. The task of each channel is to calculate the distance of the tested radar from a given class and finally, set the correct identification coefficient for each recognized radar. Thus, a multichannel system with independent distance measurement is obtained, which makes it possible to recognize particular radar copies. This system is implemented in electronic intelligence (ELINT) system and tested in real battlefield conditions.

Remote sensing image classification for Jabalpur region using swarm classifiers

Swarm intelligence algorithms have been widely applied in solving many complex problems in different domains. In this paper, swarm intelligence-based cuckoo search (CS) and artificial bee colony optimisation (ABC) are used for satellite image classification of Jabalpur region (Madhya Pradesh), India. The reason for the selection of CS and ABC algorithm over other swarm intelligence concepts is the wide and efficient applicability of these concepts in different domains. The swarm classifiers-based obtained results are compared with other than swarm intelligence techniques mainly maximum likelihood classifier (MLC), minimum distance classifier (MDC) and fuzzy logic. Results are evaluated in terms of kappa coefficient, user's accuracy, producer's accuracy and overall accuracy. Results in terms of individual feature accuracy (user's accuracy and producer's accuracy) and consolidated accuracy (kappa coefficient and overall accuracy) indicates the dominance of swarm classifiers in comparison with other considered classifiers for the classification of land cover features.

Finger contour profile based hand biometric recognition

This paper presents a contactless hand biometric system at unrestricted hand pose environment. A new preprocessing technique is proposed for defining the finger contour profiles (FCP). It mainly consists of simple grayscale image transformation, subtraction, and logical XOR operation. This hand prototyping method logically decomposes global hand contour into the left and right contour profiles of each finger. A set of twenty pose-invariant geometric features is extracted from the FCP and normalized global hand shape. Experiments are conducted on two publicly available hand databases namely, the Bosphorus and IIT Delhi (IITD) databases to validate the system using the kNN, minimum distance, and random forest (RF) classifiers. Satisfactory identification accuracy of 97.82 % using the RF classifier has been achieved for the Bosphorus database with 320 subjects; and in verification, 3.28 % equal error rate (EER) is reported. The kNN classifier has been found to produce good identification success of 95.22 % for the IITD database of 230 subjects; and 4.76 % EER is obtained in verification. The average execution time of this approach is lesser than 2 s, that implies its suitability in real-world applications.

Novel Geometrical Shape Feature Extraction Techniques for Multilingual Character Recognition

ABSTRACTMultilingual character recognition from the images of aged Indian documents is challenging because of the complex character grapheme of the Indian language scripts. Feature extraction plays the most important role in recognition of such images. In this paper, we have proposed a set of feature vectors (FVs) which are based on shape geometry (SG) decoding of the input character. The first FV is based on SG decoding of the input character using triangular area (TA) calculation. The second FV, namely, SG using perpendicular distance is extracted by dividing the input image into individual components and the shape of the individual component is decoded into shape symbols by comparing the normalized perpendicular distances of the individual pixels of the component onto the line joining the end points of the component. Apart from the proposed FVs, we have used crossing count features. These FVs are represented as the string of shape operators; hence, we have used minimum edit distance classifier to recognize the input character. The proposed character recognition technique is evaluated using the characters extracted from printed aged multilingual Indian documents having English, Devanagari, and Marathi scripts and achieved encouraging results. To further assess the performance of the proposed system, we have considered publicly available media-lab license plate benchmark database and achieved significant performance.

Sign Language Recognition System Simulated for Video Captured with Smart Phone Front Camera

<p>This works objective is to bring sign language closer to real time implementation on mobile platforms. A video database of Indian sign language is created with a mobile front camera in selfie mode. This video is processed on a personal computer by constraining the computing power to that of a smart phone with 2GB ram. Pre-filtering, segmentation and feature extraction on video frames creates a sign language feature space. Minimum distance classification of the sign feature space converts signs to text or speech. ASUS smart phone with 5M pixel front camera captures continuous sign videos containing around 240 frames at a frame rate of 30fps. Sobel edge operator’s power is enhanced with morphology and adaptive thresholding giving a near perfect segmentation of hand and head portions. Word matching score (WMS) estimates performance of the proposed method with an average WMS of around 90.58%.</p>

Sign Language Recognition System Simulated for Video Captured with Smart Phone Front Camera

<p>This works objective is to bring sign language closer to real time implementation on mobile platforms. A video database of Indian sign language is created with a mobile front camera in selfie mode. This video is processed on a personal computer by constraining the computing power to that of a smart phone with 2GB ram. Pre-filtering, segmentation and feature extraction on video frames creates a sign language feature space. Minimum distance classification of the sign feature space converts signs to text or speech. ASUS smart phone with 5M pixel front camera captures continuous sign videos containing around 240 frames at a frame rate of 30fps. Sobel edge operator’s power is enhanced with morphology and adaptive thresholding giving a near perfect segmentation of hand and head portions. Word matching score (WMS) estimates performance of the proposed method with an average WMS of around 90.58%.</p>

Examining palpebral conjunctiva for anemia assessment with image processing methods

Examining the hemoglobin level of blood is an important way to achieve the diagnosis of anemia, but it requires blood drawing and blood test. Examining the color distribution of palpebral conjunctiva is a standard procedure of anemia diagnosis, which requires no blood test. However, since color perception is not always consistent among different people, we attempt to imitate the way of physical examination of palpebral conjunctiva to detect anemia, so that computers can identify anemia patients automatically in a consolidated manner for a screening process. In this paper we propose two algorithms for anemia diagnosis. The first algorithm is intended to be simple and fast, while the second one to be more sophisticated and robust, providing an option for different applications. The first algorithm consists of a simple two-stage classifier. In the first stage, we use a thresholding decision technique based on a feature called high hue rate (HHR) (extracted from the HSI color space). In the second stage, a feature called pixel value in the middle (PVM) (extracted from the RGB color space) is proposed, followed by the use of a minimum distance classifier based on Mahalanobis distance. In the second algorithm, we consider 18 possible features, including a newly added entropy feature, some improved features from the first algorithm, and 13 features proposed in a previous work. We use correlation and simple statistics to select 3 relatively independent features (entropy, binarization of HHR, and PVM of G component) for classification using a support vector machine or an artificial neural network. Finally, we evaluate the classification performance of the proposed algorithms in terms of sensitivity, specificity, and Kappa value. The experimental results show relatively good performance and prove the feasibility of our attempt, which may encourage more follow-up study in the future.

Assessing the accuracy of hyperspectral and multispectral satellite imagery for categorical and Quantitative mapping of salinity stress in sugarcane fields

This study evaluates the feasibility of hyperspectral and multispectral satellite imagery for categorical and quantitative mapping of salinity stress in sugarcane fields located in the southwest of Iran. For this purpose a Hyperion image acquired on September 2, 2010 and a Landsat7 ETM+ image acquired on September 7, 2010 were used as hyperspectral and multispectral satellite imagery. Field data including soil salinity in the sugarcane root zone was collected at 191 locations in 25 fields during September 2010. In the first section of the paper, based on the yield potential of sugarcane as influenced by different soil salinity levels provided by FAO, soil salinity was classified into three classes, low salinity (1.7–3.4dS/m), moderate salinity (3.5–5.9dS/m) and high salinity (6–9.5) by applying different classification methods including Support Vector Machine (SVM), Spectral Angle Mapper (SAM), Minimum Distance (MD) and Maximum Likelihood (ML) on Hyperion and Landsat images. In the second part of the paper the performance of nine vegetation indices (eight indices from literature and a new developed index in this study) extracted from Hyperion and Landsat data was evaluated for quantitative mapping of salinity stress. The experimental results indicated that for categorical classification of salinity stress, Landsat data resulted in a higher overall accuracy (OA) and Kappa coefficient (KC) than Hyperion, of which the MD classifier using all bands or PCA (1–5) as an input performed best with an overall accuracy and kappa coefficient of 84.84% and 0.77 respectively. Vice versa for the quantitative estimation of salinity stress, Hyperion outperformed Landsat. In this case, the salinity and water stress index (SWSI) has the best prediction of salinity stress with an R2 of 0.68 and RMSE of 1.15dS/m for Hyperion followed by Landsat data with an R2 and RMSE of 0.56 and 1.75dS/m respectively. It was concluded that categorical mapping of salinity stress is the best option for monitoring agricultural fields and for this purpose Landsat data are most suitable.

Research on dynamic heuristic scanning technique and the application of the malicious code detection model

With the rapid development of computer technology, people pay more attention to the security of computer data and the computer virus has become a chief threat to computer data security. By using an antivirus system that can identify randomly generated computer viruses and on the basis of the basic characteristics of the computer code, this paper investigates the heuristic scanning technique. This paper proposes the minimum distance classifier and detection model through the analysis of the malicious code. This model can identify unknown feature codes of illegal procedures and construct a healthy network environment by using a combination of model and experimental method, which can intercept the illegal virus program in the installation and operation stages.

GLOBAL LAND COVER CLASSIFICATION USING MODIS SURFACE REFLECTANCE PROSUCTS

The objective of this study is to develop high accuracy land cover classification algorithm for Global scale by using multi-temporal MODIS land reflectance products. In this study, time-domain co-occurrence matrix was introduced as a classification feature which provides time-series signature of land covers. Further, the non-parametric minimum distance classifier was introduced for timedomain co-occurrence matrix, which performs multi-dimensional pattern matching for time-domain co-occurrence matrices of a classification target pixel and each classification classes. The global land cover classification experiments have been conducted by applying the proposed classification method using 46 multi-temporal(in one year) SR(Surface Reflectance) and NBAR(Nadir BRDF-Adjusted Reflectance) products, respectively. IGBP 17 land cover categories were used in our classification experiments. As the results, SR and NBAR products showed similar classification accuracy of 99%.

GLOBAL LAND COVER CLASSIFICATION USING MODIS SURFACE REFLECTANCE PROSUCTS

Abstract. The objective of this study is to develop high accuracy land cover classification algorithm for Global scale by using multi-temporal MODIS land reflectance products. In this study, time-domain co-occurrence matrix was introduced as a classification feature which provides time-series signature of land covers. Further, the non-parametric minimum distance classifier was introduced for timedomain co-occurrence matrix, which performs multi-dimensional pattern matching for time-domain co-occurrence matrices of a classification target pixel and each classification classes. The global land cover classification experiments have been conducted by applying the proposed classification method using 46 multi-temporal(in one year) SR(Surface Reflectance) and NBAR(Nadir BRDF-Adjusted Reflectance) products, respectively. IGBP 17 land cover categories were used in our classification experiments. As the results, SR and NBAR products showed similar classification accuracy of 99%.

A feed-forward Hopfield neural network algorithm (FHNNA) with a colour satellite image for water quality mapping

There are many techniques that have been given for water quality problem, but the remote sensing techniques have proven their success, especially when the artificial neural networks are used as mathematical models with these techniques. Hopfield neural network is one type of artificial neural networks which is common, fast, simple, and efficient, but it when it deals with images that have more than two colours such as remote sensing images. This work has attempted to solve this problem via modifying the network that deals with colour remote sensing images for water quality mapping. A Feed-forward Hopfield Neural Network Algorithm (FHNNA) was modified and used with a satellite colour image from type of Thailand earth observation system (THEOS) for TSS mapping in the Penang strait, Malaysia, through the classification of TSS concentrations. The new algorithm is based essentially on three modifications: using HNN as feed-forward network, considering the weights of bitplanes, and non-self-architecture or zero diagonal of weight matrix, in addition, it depends on a validation data. The achieved map was colour-coded for visual interpretation. The efficiency of the new algorithm has found out by the higher correlation coefficient (R=0.979) and the lower root mean square error (RMSE=4.301) between the validation data that were divided into two groups. One used for the algorithm and the other used for validating the results. The comparison was with the minimum distance classifier. Therefore, TSS mapping of polluted water in Penang strait, Malaysia, can be performed using FHNNA with remote sensing technique (THEOS). It is a new and useful application of HNN, so it is a new model with remote sensing techniques for water quality mapping which is considered important environmental problem.

Random Forest Algorithm for Land Cover Classification

Since the launch of the first land observation satellite Landsat-1 in 1972, many machine learning algorithms have been used to classify pixels in Thematic Mapper (TM) imagery. Classification methods range from parametric supervised classification algorithms such as maximum likelihood, unsupervised algorithms such as ISODAT and k-means clustering to machine learning algorithms such as artificial neural, decision trees, support vector machines, and ensembles classifiers. Various ensemble classification algorithms have been proposed in recent years. Most widely used ensemble classification algorithm is Random Forest. The Random Forest classifier uses bootstrap aggregating for form an ensemble of classification and induction tree like tree classifiers. A few researchers have used Random Forest for land cover analysis. However, the potential of Random Forest has not yet been fully explored by the remote sensing community. In this paper we compare classification accuracy of Random Forest with other commonly used algorithms such as the maximum likelihood, minimum distance, decision tree, neural network, and support vector machine classifiers.

A study on gait entropy image analysis for clothing invariant human identification

A simple and common human gait may be viewed as a strong biometric cue to solve human identification problem through understanding the intrinsic patterns of gait biometrics. An individual's gait pattern appears to be different in gallery and probe gait sequences due to wearing dissimilar clothing types. The gait dataset captures the possible changes found in silhouette shape image which provides the difficulty in distinguishing among individuals. In this paper, a robust feature selection technique has been addressed through Gait Entropy Image (GEnI) analysis. The GEnI has the capacity to accumulate most significant motion information. The width of GEnI, along the horizontal axis is taken as discriminative feature which produces a small intra-class variance. This information is studied as an evidence of feature invariance. The standard statistical tests such as pair-wise clothing correlation and intra-clothing variance are performed on gait dataset to evaluate the reliability of feature. Experimental results demonstrate the efficiency of proposed feature selection method using k-nearest neighbor (k-NN), minimum distance classifier (MDC), and support vector machine (SVM) algorithms. The performance analysis of recognition system has been evaluated on OU-ISIR Treadmill B gait database with different error metrics after performing N-fold cross validation method.

Monitoring Plastic-Mulched Farmland by Landsat-8 OLI Imagery Using Spectral and Textural Features

In recent decades, plastic-mulched farmland has expanded rapidly in China as well as in the rest of the world because it results in marked increases of crop production. However, plastic-mulched farmland significantly influences the environment and has so far been inadequately investigated. Accurately monitoring and mapping plastic-mulched farmland is crucial for agricultural production, environmental protection, resource management, and so on. Monitoring plastic-mulched farmland using moderate-resolution remote sensing data is technically challenging because of spatial mixing and spectral confusion with other ground objects. This paper proposed a new scheme that combines spectral and textural features for monitoring the plastic-mulched farmland and evaluates the performance of a Support Vector Machine (SVM) classifier with different kernel functions using Landsat-8 Operational Land Imager (OLI) imagery. The textural features were extracted from multi-bands OLI data using a Grey Level Co-occurrence Matrix (GLCM) algorithm. Then, six combined feature sets were developed for classification. The results indicated that Landsat-8 OLI data are well suitable for monitoring plastic-mulched farmland; the SVM classifier with a linear kernel function is superior both to other kernel functions and to two other widely used supervised classifiers: Maximum Likelihood Classifier (MLC) and Minimum Distance Classifier (MDC). For the SVM classifier with a linear kernel function, the highest overall accuracy was derived from combined spectral and textural features in the 90° direction (94.14%, kappa 0.92), followed by the combined spectral and textural features in the 45° (93.84%, kappa 0.92), 135° (93.73%, kappa 0.92), 0° (93.71%, kappa 0.92) directions, and the spectral features alone (93.57%, kappa 0.91). Spectral features make a more significant contribution to monitoring the plastic-mulched farmland; adding textural features from medium resolution imagery provide only limited improvement in accuracy.

Examining region-based methods for land cover classification using stochastic distances

ABSTRACTA recent alternative to standard pixel-based classification of remote-sensing data is region-based classification, which has proved to be particularly useful when analysing high-resolution imagery of complex environments, such as urban areas, or when addressing noisy data, such as synthetic aperture radar (SAR) images. First, following certain criteria, the imagery is decomposed into homogeneous regions, and then each region is classified into a class of interest. The usual method for region-based classification involves using stochastic distances, which measure the distances between the pixel distributions inside an unknown region and the representative distributions of each class. The class, which is at the minimum distance from the unknown region distribution, is assigned to the region and this procedure is termed stochastic minimum distance classification (SMDC). This study reports the use of methods derived from the original SMDC, Support Vector Machine (SVM), and graph theory, with the objective of identifying the most robust and accurate classification methods. The equivalent pixel-based versions of region-based analysed methods were included for comparison. A case study near the Tapajós National Forest, in Pará state, Brazil, was investigated using ALOS PALSAR data. This study showed that methods based on the nearest neighbour, derived from SMDC, and SVM, with a specific kernel function, are more accurate and robust than the other analysed methods for region-based classification. Furthermore, pixel-based methods are not indicated to perform the classification of images with a strong presence of noise, such as SAR images.

A combination of chemometrics methods and GC–MS for the classification of edible vegetable oils

The authenticity of edible vegetable oils is a very important issue due to consumer health and commercial reasons. Gas chromatography–mass spectrometry (GC–MS) was applied to analyze the fatty acid composition of sixty six samples from six different kinds of edible vegetable oils. The fatty acid profiles of these edible vegetable oils were used to classify the type of edible oils. For improving the classification accuracy of vegetable oils with respect to type, the support vector machine (SVM) technique, optimized using the genetic algorithm (GA), was employed to construct the classification model. The effectiveness of the GA–SVM combination in classification was compared with that of other well-known strategies for classification, such as minimum distance classification (MDC) and linear discriminant analysis (LDA). In addition, the Kennard–Stone algorithm was used to select the representative training samples and compared with the random sampling method. The misclassification rates were 8.48% and 3.03% for training and test set, respectively, by the GA–SVM model using the linear kernel. Only one or two samples will be misclassified in the process of GA–SVM classification. The classification task based on fatty acid data can be successfully achieved by the GA–SVM technique combined with the Kennard–Stone algorithm. The results reveal that this strategy is of great promise in flexible and accurate classification of edible vegetable oils.

Multimodal Recognition Method based on Ear and Profile Face Feature Fusion

The performance of ear recognition is influenced by pose variation. For the similar position of ear and profile face, a multimodal recognition method is proposed based on the feature fusion of ear and profile face information. A model for ear and profile face feature fusion and recognition is built. The Log-Gabor features of ear and profile face are first extracted separately, and two features are integrated into a combined feature after two Log-Gabor features are standardized. Then combined feature is mapped to kernel space to fuse further, and acquired stronger discriminant feature for classification by kernel Fisher discriminant analysis (KFDA). The minimum distance classifier is finally used in recognition. Experimental results on the profile face database of Notre Dame University show that the fused method improves the recognition rate of pose variation, and the performance of multimodal recognition is better than unimodal recognition using either ear or profile face alone. The method of ear and profile face feature fusion and recognition is effective and robust for the pose variation.

Imaging for High-Throughput Phenotyping in Energy Sorghum

The increasing energy demand in recent years has resulted in a continuous growing interest in renewable energy sources, such as efficient and high-yielding energy crops. Energy sorghum is a crop that has shown great potential in this area, but needs further improvement. Plant phenotyping—measuring physiological characteristics of plants—is a laborious and time-consuming task, but it is essential for crop breeders as they attempt to improve a crop. The development of high-throughput phenotyping (HTP)—the use of autonomous sensing systems to rapidly measure plant characteristics—offers great potential for vastly expanding the number of types of a given crop plant surveyed. HTP can thus enable much more rapid progress in crop improvement through the inclusion of more genetic variability. For energy sorghum, stalk thickness is a critically important phenotype, as the stalk contains most of the biomass. Imaging is an excellent candidate for certain phenotypic measurements, as it can simulate visual observations. The aim of this study was to evaluate image analysis techniques involving K-means clustering and minimum-distance classification for use on red-green-blue (RGB) images of sorghum plants as a means to measure stalk thickness. Additionally, a depth camera integrated with the RGB camera was tested for the accuracy of distance measurements between camera and plant. Eight plants were imaged on six dates through the growing season, and image segmentation, classification and stalk thickness measurement were performed. While accuracy levels with both image analysis techniques needed improvement, both showed promise as tools for HTP in sorghum. The average error for K-means with supervised stalk measurement was 10.7% after removal of known outliers.

Feature Selection Based on Enhanced Cuckoo Search for Breast Cancer Classification in Mammogram Image

Proposed system has been developed to extract the optimal features from the breast tumors using Enhanced Cuckoo Search (ECS) and presented in this paper. The texture feature, intensity histogram feature, radial distance feature and shape features have been extracted and the optimal feature set has been obtained using ECS. The overall accuracy of a minimum distance classifier and k-Nearest Neighbor (k-NN) on validation samples is used as a fitness value for ECS. The new approach is carried out on the extracted feature dataset. The proposed system selects only the minimum number of features and performed the accuracy of 98.75% with Minimum Distance Classifier and 99.13% with k-NN Classifier. The performance of the new ECS is compared with the Cuckoo Search and Harmony Search. This result shows that the ECS algorithm is more accurate than the other algorithm. The proposed system can provide valuable information to the physician in medical pathology.