Nearest-neighbor Classification Method Research Articles

Shared style linear k nearest neighbor classification method

While previous linear k nearest neighbor methods have demonstrated strong generalization capability for ordinary datasets, they often fail to achieve comparable generalization capability when applied to stylistic datasets due to the unique characteristics of this type of datasets. Additionally, when multiple testing samples share a certain style characteristic, both the traditional k nearest neighbor method and evolved versions of the linear k nearest neighbor method tend to ignore the shared style in the testing sample set, leading to inefficient utilization of information resources. In order to address these issues, we propose a new classification method called Shared Style Linear kNearest Neighbor (SSL-KNN), which enhances the generalization capability of the linear k nearest neighbor method on stylistic datasets. The proposed method is motivated by the observation that one can better determine the class of a collection of homogeneous samples by evaluating their overall style, rather than simply aggregating individual sample judgments. The proposed method achieves this goal and two additional goals: a) we obtain the style membership vectors of the testing samples with different classes of the training samples in order to seek a guarantee of the generalization capability on the stylistic datasets; b) we introduce a Gaussian kernel distance metric constraining the linear expression weights in order to seek a guarantee of the generalization capability on the ordinary datasets. Furthermore, we propose an alternating optimization strategy implemented to optimize the proposed SSL-KNN method. Finally, we evaluate the proposed method and the comparative methods on 15 benchmark datasets containing both ordinary and stylistic datasets. The results show that the proposed SSL-KNN method guarantees good generalization capability on both ordinary and stylistic datasets.

Non-parametric Nearest Neighbor Classification Based on Global Variance Difference

As technology improves, how to extract information from vast datasets is becoming more urgent. As is well known, k-nearest neighbor classifiers are simple to implement and conceptually simple to implement. It is not without its shortcomings, however, as follows: (1) there is still a sensitivity to the choice of k-values even when representative attributes are not considered in each class; (2) in some cases, the proximity between test samples and nearest neighbor samples cannot be reflected accurately due to proximity measurements, etc. Here, we propose a non-parametric nearest neighbor classification method based on global variance differences. First, the difference in variance is calculated before and after adding the sample to be the subject, then the difference is divided by the variance before adding the sample to be tested, and the resulting quotient serves as the objective function. In the final step, the samples to be tested are classified into the class with the smallest objective function. Here, we discuss the theoretical aspects of this function. Using the Lagrange method, it can be shown that the objective function can be optimal when the sample centers of each class are averaged. Twelve real datasets from the University of California, Irvine are used to compare the proposed algorithm with competitors such as the Local mean k-nearest neighbor algorithm and the pseudo-nearest neighbor algorithm. According to a comprehensive experimental study, the average accuracy on 12 datasets is as high as 86.27%, which is far higher than other algorithms. The experimental findings verify that the proposed algorithm produces results that are more dependable than other existing algorithms.

Research on Estimation of Paddy Field Area Index Based on UAV Remote Sensing Images

In order to verify the superiority and effectiveness of extracting rice information based on UAV images. This paper takes the rice plot as the research object, and uses the portable UAV Mavic Pro for aerial photography. Preprocess the acquired UAV images to generate orthophotos with a resolution of 3.95cm/pix. Using object-oriented thinking, visual evaluation and ESP tools are combined to quickly select the optimal segmentation scale to be 300, and support is applied. Vector machine, random forest, and nearest neighbor supervised classification methods have carried out ground object classification and rapid extraction of rice area. The classification results and area accuracy are evaluated by visual classification results. The method with the highest overall accuracy is the nearest neighbor classification method. At this time, the user accuracy of rice classification is 95%, and the area consistency accuracy is 99%. The results show that UAV remote sensing and automatic classification can quickly obtain high resolution images and extract rice planting area in plain rice planting area, make up for the lack of ground survey data when Nongshan is blocked, and provide samples and verification basis for the calculation of large-scale rice planting area, yield and other information.

Image Classification and Detection of Insulators using Bag of Visual Words and Speeded up Robust Features

Electrical substation online monitoring in computer vision technology is based on image processing algorithm to perform visual analysis. This paper presents classification of ceramicand glass insulators through Bag of Visual Words and detection of these insulators by Point Feature Matching. The training image datasets are used for categorization by forming a visual vocabulary while a new unlabeled image from testing image dataset is classify using nearest neighbor classification method for features descriptor. For detection we use Speeded up Robust Features for detecting position of insulator present in cluttered scene image. Matching process is done between test and reference image and decision is made based on similar features. We conducted experiment on insulators to verify the superiority of our proposed method. The proposed method can be used in security, surveillance and inspection system

Hybrid Ensemble Deep Learning for Deterministic and Probabilistic Low-Voltage Load Forecasting

Accurate and reliable low-voltage load forecasting is critical to optimal operation and control of distribution network and smart grid. However, compared to traditional regional load forecasting at high-voltage level, it faces tough challenges due to the inherent high uncertainty of the low-capacity load and distributed renewable energy integrated in the demand side. This paper proposes a novel hybrid ensemble deep learning (HEDL) approach for deterministic and probabilistic low-voltage load forecasting. The deep belief network (DBN) is applied to low-voltage load point prediction with the strong ability of approximating nonlinear mapping. A series of ensemble learning methods including bagging and boosting variants are introduced to improve the regression ability of DBN. In addition, the differencing transformation technique is utilized to ensure the stationarity of load time series for the application bagging and boosting methods. On the basis of the integrated thought of ensemble learning, a new hybrid ensemble algorithm is developed via integrating multiple separate ensemble methods. Considering the diversity in various ensemble algorithms, an effective K nearest neighbor classification method is utilized to adaptively determine the weights of sub-models. Furthermore, HEDL based probabilistic forecasting is proposed by taking advantage of the inherent resample idea in bagging and boosting. The effectiveness of the HEDL method for both deterministic and probabilistic forecasting has been systematically verified based on realistic load data from East China and Australia, indicating its promising prospective for practical applications in distribution networks.

Landslides Information Extraction Using Object-Oriented Image Analysis Paradigm Based on Deep Learning and Transfer Learning

How to acquire landslide disaster information quickly and accurately has become the focus and difficulty of disaster prevention and relief by remote sensing. Landslide disasters are generally featured by sudden occurrence, proposing high demand for emergency data acquisition. The low-altitude Unmanned Aerial Vehicle (UAV) remote sensing technology is widely applied to acquire landslide disaster data, due to its convenience, high efficiency, and ability to fly at low altitude under cloud. However, the spectrum information of UAV images is generally deficient and manual interpretation is difficult for meeting the need of quick acquisition of emergency data. Based on this, UAV images of high-occurrence areas of landslide disaster in Wenchuan County and Baoxing County in Sichuan Province, China were selected for research in the paper. Firstly, the acquired UAV images were pre-processed to generate orthoimages. Subsequently, multi-resolution segmentation was carried out to obtain image objects, and the barycenter of each object was calculated to generate a landslide sample database (including positive and negative samples) for deep learning. Next, four landslide feature models of deep learning and transfer learning, namely Histograms of Oriented Gradients (HOG), Bag of Visual Word (BOVW), Convolutional Neural Network (CNN), and Transfer Learning (TL) were compared, and it was found that the TL model possesses the best feature extraction effect, so a landslide extraction method based on the TL model and object-oriented image analysis (TLOEL) was proposed; finally, the TLOEL method was compared with the object-oriented nearest neighbor classification (NNC) method. The research results show that the accuracy of the TLOEL method is higher than the NNC method, which can not only achieve the edge extraction of large landslides, but also detect and extract middle and small landslides accurately that are scatteredly distributed.

Image Classification Using Bag of Visual Words (BoVW)

In this paper two main stages for image classification has been presented. Training stage consists of collecting images of interest, and apply BOVW on these images (features extraction and description using SIFT, and vocabulary generation), while testing stage classifies a new unlabeled image using nearest neighbor classification method for features descriptor. Supervised bag of visual words gives good result that are present clearly in the experimental part where unlabeled images are classified although small number of images are used in the training process.

Analyzing Trends of Dike-Ponds between 1978 and 2016 Using Multi-Source Remote Sensing Images in Shunde District of South China

Dike-ponds have experienced significant changes in the Pearl River Delta region over the past several decades, especially since China’s economic reform, which has seriously affected the construction of ecological environments. In order to monitor the evolution of dike-ponds, in this study we use multi-source remote sensing images from 1978 to 2016 to extract dike-ponds in several periods using the nearest neighbor classification method. A corresponding area weighted dike-pond invasion index (AWDII) is proposed to describe the spatial evolution of dike-ponds, both qualitatively and quantitatively. Furthermore, the evolution mechanisms of dike-ponds are determined, which can be attributed to both natural conditions and human factors. Our results show that the total area of dike-ponds in 2016 was significantly reduced and fragmentation had increased compared with the situation in 1978. The AWDII reveals that Shunde District has experienced three main phases, including steady development, rapid invasion and a reduction of invasion by other land use types. Most dike-ponds have now converted into built-up areas, followed by cultivated lands, mainly due to government policies, rural area depopulation, and river networks within Shunde. Our study indicates that the AWDII is applicable towards the evaluation of the dynamic changes of dike-ponds. The rational development, and careful protection, of dike-ponds should be implemented for better land and water resource management.

基于K-Means的时间季节反演识别方法

不同的时间季节有不同的特征，根据季节的特征，我们在计算机上利用各种方法可以清楚的识别不同的季节。本文利用HSV图像色彩模型，主要对各个季节的图片的特征进行分析和研究，进行颜色间的对比，比较其色彩值的平均值与方差，运用光学图像处理技术，采用近邻分类的方法，对比各季节图像的特征差异，对所选图像进行分类与识别，进而达到自动识别季节的目的。实验结果证实了此研究方法的可行性，在季节识别上能够达到自动识别的效果。 Different time seasons have different characteristics. According to the characteristics of the season, we use various methods on the computer to clearly identify different seasons. Based on HSV color model, the characteristics of the images of each season are analyzed and studied, and in the colors contrast, the mean value and variance of them are compared. Through using the optical image processing technology, adopting the nearest neighbor classification method, comparing the characteristics of different season images, and then classifying and recognizing the selected images, thus the season can be identified automatically. The experimental results confirm the feasibility of this method, and can achieve high performance in season recognition.

A new nearest neighbor classification method based on fuzzy set theory and aggregation operators

The Fuzzy Nearest Neighbor Classification (FuzzyNNC) has been successfully used, as a tool to deal with supervised classification problems. It has significantly increased the classification accuracy by considering the uncertainty associated with the class labels of the training patterns. Nevertheless, FuzzyNNC's limited methods fail to efficiently handle the imprecision in features measurement and the uncertainty induced by the choice of the distance measure and the number of neighbors in the decision rule. In this paper, we propose a new method called Fuzzy Analogy-based Classification (FABC) to tackle the FuzzyNNC limitations. In this work, we exploit the fuzzy linguistic modeling and approximate reasoning materials in order to endow FABC with intelligent capabilities, like imprecision tolerance, optimization, adaptability and trade-off. Hence, our approach is composed of two main steps. Firstly, we describe the domain features using fuzzy linguistic variables. Secondly, we define the classification process using two intelligent aggregation operators. The first one allows the optimization of the similarity evaluation, by defining the adequate features to be considered. The second one integrates a trade-off strategy within the decision rule, by using a global voting approach with compensation property. The integration of such mechanisms will increase the classification accuracy and make the FuzzyNNC approach more useful for classification problems where imprecision and uncertainty are unavoidable. The proposed FABC is validated on the most known datasets, representing various classification difficulties and compared to the many extensions of the FuzzyNNC approach. The results obtained show that our proposed FABC method can be adapted to different classification problems and improve the classification accuracy. Thus, the FABC has the best rank value against the comparison methods with high significant level. Moreover, we conclude that our optimized similarity and global voting rule are more robust to handle the uncertainty in the classification process than those used by the comparison methods.

Face Representation and Face Recognition using Optimized Local Ternary Patterns (OLTP)

For many years, researchers in face description area have been representing and recognizing faces based on different methods that include subspace discriminant analysis, statistical learning and non-statistics based approach etc. But still automatic face recognition remains an interesting but challenging problem. This paper presents a novel and efficient face image representation method based on Optimized Local Ternary Pattern (OLTP) texture features. The face image is divided into several regions from which the OLTP texture feature distributions are extracted and concatenated into a feature vector that can act as face descriptor. The recognition is performed using nearest neighbor classification method with Chi-square distance as a similarity measure. Extensive experimental results on Yale B, ORL and AR face databases show that OLTP consistently performs much better than other well recognized texture models for face recognition.

Aero-engine rotor-stator rubbing position identification based on casing velocity signal

A rotor-stator rubbing position identification method based on casing velocity signal is proposed. Considering that velocity is an ideal parameter to reflect vibration, and its effective value is a standard to the measuring of vibration fault diagnosis in the world, firstly, the rotor experiment rig of aero-engine was used to simulate rubbing faults in different rubbing positions, and casing vibration acceleration signal was collected and changed to velocity signal through integral and polynomial least square fitting method. Secondly, low-frequency normalized energy characteristics of velocity signal and normalized mean-square value characteristics of acceleration signal were extracted; finally, normalized characteristic parameters including energy and mean-square value were input to nearest neighbor classifier and support vector machine(SVM) to identify the different rubbing positions. The results show that low-frequency energy characteristics of velocity signal can effectively identify the rotor-stator rubbing positions of aero-engine, and reach to 93 % of recognition rate based on nearest neighbor classification method and 98 % based on SVM, while mean-square value characteristics of acceleration signal recognition rate can only reach 81 % based on nearest neighbor algorithm and 85 % based on SVM.

A locally adaptive kernel regression method for facies delineation

Summary Facies delineation is defined as the separation of geological units with distinct intrinsic characteristics (grain size, hydraulic conductivity, mineralogical composition). A major challenge in this area stems from the fact that only a few scattered pieces of hydrogeological information are available to delineate geological facies. Several methods to delineate facies are available in the literature, ranging from those based only on existing hard data, to those including secondary data or external knowledge about sedimentological patterns. This paper describes a methodology to use kernel regression methods as an effective tool for facies delineation. The method uses both the spatial and the actual sampled values to produce, for each individual hard data point, a locally adaptive steering kernel function, self-adjusting the principal directions of the local anisotropic kernels to the direction of highest local spatial correlation. The method is shown to outperform the nearest neighbor classification method in a number of synthetic aquifers whenever the available number of hard data is small and randomly distributed in space. In the case of exhaustive sampling, the steering kernel regression method converges to the true solution. Simulations ran in a suite of synthetic examples are used to explore the selection of kernel parameters in typical field settings. It is shown that, in practice, a rule of thumb can be used to obtain suboptimal results. The performance of the method is demonstrated to significantly improve when external information regarding facies proportions is incorporated. Remarkably, the method allows for a reasonable reconstruction of the facies connectivity patterns, shown in terms of breakthrough curves performance.

Design of Gait Recognition System Based on FPGA and DSP

According to the trend of the intelligent monitoring system, on the basis of the study of gait recognition algorithm, the intelligent monitoring system is designed based on FPGA and DSP; On the one hand, FPGA’s flexibility and fast parallel processing algorithms when designing can be both used to avoid that circuit can not be modified after designed; On the other hand, the advantage of processing the digital signal of DSP is fully taken. In the feature extraction and recognition, Zernike moment is selected, at the same time the system uses the nearest neighbor classification method which is more mature and has good real-time performance. Experiments show that the system has high recognition rate.

Ultra high content image analysis and phenotype profiling of 3D cultured micro-tissues.

In many situations, 3D cell cultures mimic the natural organization of tissues more closely than 2D cultures. Conventional methods for phenotyping such 3D cultures use either single or multiple simple parameters based on morphology and fluorescence staining intensity. However, due to their simplicity many details are not taken into account which limits system-level study of phenotype characteristics. Here, we have developed a new image analysis platform to automatically profile 3D cell phenotypes with 598 parameters including morphology, topology, and texture parameters such as wavelet and image moments. As proof of concept, we analyzed mouse breast cancer cells (4T1 cells) in a 384-well plate format following exposure to a diverse set of compounds at different concentrations. The result showed concentration dependent phenotypic trajectories for different biologically active compounds that could be used to classify compounds based on their biological target. To demonstrate the wider applicability of our method, we analyzed the phenotypes of a collection of 44 human breast cancer cell lines cultured in 3D and showed that our method correctly distinguished basal-A, basal-B, luminal and ERBB2+ cell lines in a supervised nearest neighbor classification method.

A novel method for identifying rotor-stator rubbing positions using the cepstrum analysis technique

A novel method of aero-engine rubbing positions identification based on cepstrum analysis is proposed, and the transfer path characteristics which reflect the transfer characteristics information from rubbing points to casing measuring points are separated from the vibration acceleration signals of casing by means of cepstrum analysis. Therefore, there is different transfer characteristics information at different rubbing positions, and in view of this, twenty rubbing positions identification features from the cepstrum are extracted. A large number of rubbing experiments of different positions are simulated with the rotor experiment rig of aero-engine, and the characteristic analysis of experimental samples at different rubbing positions is carried out, and the results indicate the consistency of features to the same rubbing position and the difference of the features to the different rubbing positions. Finally, the aero-engine rubbing positions identification is carried out using the nearest neighbor classification method, the recognition rate reaches 100%, and the effectiveness of the method is full verified.

Research of Small Current Grounding Fault Classification Based on Phase Plane Method

In order to solve the problem of single-phase ground fault classification, phase plane method is used. We use phase plane method to analysis single-phase ground fault waveform data first, so a series of discrete points is getting. The discrete points are divided by region on the phase plane to statistics the numbers in each region, so these numbers constitute the feature vector what we required. Use nearest neighbor classification method to achieve single-phase ground fault data classification. Empirical results show that this method can divided the faults into five to find fault reason, thereby improving the diagnostic capability of fault status and fault reason in distribution network.

A bumble bees mating optimization algorithm for the feature selection problem

The feature selection problem is an interesting and important topic which is relevant for a variety of database applications. This paper utilizes a relatively new bees inspired optimization algorithm, the bumble bees mating optimization algorithm, to implement a feature subset selection procedure while the nearest neighbor classification method is used for the classification task. Several metrics are used in the nearest neighbor classification method, such as the euclidean distance, the standardized euclidean distance, the mahalanobis distance, the city block metric, the cosine distance and the correlation distance, in order to identify the most significant metric for the nearest neighbor classifier. The performance of the proposed algorithm is tested using various benchmark data sets from the UCI machine learning repository. The algorithm is compared with two other bees inspired algorithms, the one is based on the foraging behavior of the bees, the discrete artificial bee colony, and the other is based on the mating behavior of the bees, the honey bees mating optimization algorithm. The algorithm is, also, compared with a particle swarm optimization algorithm, an ant colony optimization algorithm, a genetic algorithm and with a number of algorithms from the literature.

A novel approach of ship wakes target classification based on the LBP-IBPANN algorithm

The detection of ship wakes image can demonstrate substantial information regarding on a ship, such as its tonnage, type, direction, and speed of movement. Consequently, the wake target recognition is a favorable way for ship identification. This paper proposes a Local Binary Pattern (LBP) approach to extract image features (wakes) for training an Improved Back Propagation Artificial Neural Network (IBPANN) to identify ship speed. This method is applied to sort and recognize the ship wakes of five different speeds images, the result shows that the detection accuracy is satisfied as expected, the average correctness rates of wakes target recognition at the five speeds may be achieved over 80%. Specifically, the lower ship's speed, the better accurate rate, sometimes it's accuracy could be close to 100%. In addition, one significant feature of this method is that it can receive a higher recognition rate than the nearest neighbor classification method.

Identifying Rotor-stator Rubbing Positions Using the Cepstrum Analysis Technique

The method of aero-engine rubbing positions identification based on cepstrum analysis is proposed. The transfer path characteristics which reflect the transfer characteristics information from rubbing points to casing measuring points are separated from the vibration acceleration signals of casing by means of cepstrum analysis. Therefore, there is different transfer characteristics information at different rubbing positions, and in view of this, twenty rubbing positions identification features are extracted. A large number of rubbing experiments of different positions are simulated with the rotor experiment rig of aero-engine, and the characteristic analysis of experimental samples at different rubbing positions is carried out, and the results indicate the consistency of features to the same rubbing position and the difference of the features to the different rubbing positions. Aero-engine rubbing positions identification is carried out using the nearest neighbor classification method. The recognition rate reaches 100%, and the effectiveness of the method is full verified.