Video Summarization Approach Based on Binary Robust Invariant Scalable Keypoints and Bisecting K-Means

Due to the exponential growth of video data,aided by rapid advancements in multimedia technologies.It became difficult for the user to obtain information from a large video series.The process of providing an abstract of the entire video that includes the most representative frames is known as static video summarization.This method resulted in rapid exploration,indexing,and retrieval of massive video libraries.We propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint(BRISK)and bisecting K-means clustering algorithm.The current method effectively recognizes relevant frames using BRISK by extracting keypoints and the descriptors from video sequences.The video frames’BRISK features are clustered using a bisecting K-means,and the keyframe is determined by selecting the frame that is most near the cluster center.Without applying any clustering parameters,the appropriate clusters number is determined using the silhouette coefficient.Experiments were carried out on a publicly available open video project(OVP)dataset that contained videos of different genres.The proposed method’s effectiveness is compared to existing methods using a variety of evaluation metrics,and the proposed method achieves a trade-off between computational cost and quality.

Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation

Several pests feed on leaves,stems,bases,and the entire plant,causing plant illnesses.As a result,it is vital to identify and eliminate the disease before causing any damage to plants.Manually detecting plant disease and treating it is pretty challenging in this period.Image processing is employed to detect plant disease since it requires much effort and an extended processing period.The main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases,including Phytophthora infestans,Fusarium graminearum,Puccinia graminis,tomato yellow leaf curl.Therefore,this work uses the Support vector machine(SVM)classifier to detect and classify the plant disease using various steps like image acquisition,Pre-processing,Segmentation,feature extraction,and classification.The gray level co-occurrence matrix(GLCM)and the local binary pattern features(LBP)are used to identify the disease-affected portion of the plant leaf.According to experimental data,the proposed technology can correctly detect and diagnose plant sickness with a 97.2 percent accuracy.

Genetic Algorithm Combined with the K-Means Algorithm:A Hybrid Technique for Unsupervised Feature Selection

The dimensionality of data is increasing very rapidly,which creates challenges for most of the current mining and learning algorithms,such as large memory requirements and high computational costs.The literature includes much research on feature selection for supervised learning.However,feature selection for unsupervised learning has only recently been studied.Finding the subset of features in unsupervised learning that enhances the performance is challenging since the clusters are indeterminate.This work proposes a hybrid technique for unsupervised feature selection called GAk-MEANS,which combines the genetic algorithm(GA)approach with the classical k-Means algorithm.In the proposed algorithm,a new fitness func-tion is designed in addition to new smart crossover and mutation operators.The effectiveness of this algorithm is demonstrated on various datasets.Fur-thermore,the performance of GAk-MEANS has been compared with other genetic algorithms,such as the genetic algorithm using the Sammon Error Function and the genetic algorithm using the Sum of Squared Error Function.Additionally,the performance of GAk-MEANS is compared with the state-of-the-art statistical unsupervised feature selection techniques.Experimental results show that GAk-MEANS consistently selects subsets of features that result in better classification accuracy compared to others.In particular,GAk-MEANS is able to significantly reduce the size of the subset of selected features by an average of 86.35%(72%–96.14%),which leads to an increase of the accuracy by an average of 3.78%(1.05%–6.32%)compared to using all features.When compared with the genetic algorithm using the Sammon Error Function,GAk-MEANS is able to reduce the size of the subset of selected features by 41.29%on average,improve the accuracy by 5.37%,and reduce the time by 70.71%.When compared with the genetic algorithm using the Sum of Squared Error Function,GAk-MEANS on average is able to reduce the size of the subset of selected features by 15.91%,and improve the accuracy by 9.81%,but the time is increased by a factor of 3.When compared with the machine-learning based methods,we observed that GAk-MEANS is able to increase the accuracy by 13.67%on average with an 88.76%average increase in time.

Improved k-means clustering algorithm

In allusion to the disadvantage of having to obtain the number of clusters of data sets in advance and the sensitivity to selecting initial clustering centers in the k-means algorithm, an improved k-means clustering algorithm is proposed. First, the concept of a silhouette coefficient is introduced, and the optimal clustering number Kopt of a data set with unknown class information is confirmed by calculating the silhouette coefficient of objects in clusters under different K values. Then the distribution of the data set is obtained through hierarchical clustering and the initial clustering-centers are confirmed. Finally, the clustering is completed by the traditional k-means clustering. By the theoretical analysis, it is proved that the improved k-means clustering algorithm has proper computational complexity. The experimental results of IRIS testing data set show that the algorithm can distinguish different clusters reasonably and recognize the outliers efficiently, and the entropy generated by the algorithm is lower.

An efficient enhanced k-means clustering algorithm

In k-means clustering, we are given a set of n data points in d-dimensional space R^d and an integer k and the problem is to determine a set of k points in R^d, called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this paper, we present a simple and efficient clustering algorithm based on the k-means algorithm, which we call enhanced k-means algorithm. This algorithm is easy to implement, requiring a simple data structure to keep some information in each iteration to be used in the next iteration. Our experimental results demonstrated that our scheme can improve the computational speed of the k-means algorithm by the magnitude in the total number of distance calculations and the overall time of computation.

Estimating wheat fractional vegetation cover using a density peak k-means algorithm based on hyperspectral image data

Fractional vegetation cover(FVC)is an important parameter to measure crop growth.In studies of crop growth monitoring,it is very important to extract FVC quickly and accurately.As the most widely used FVC extraction method,the photographic method has the advantages of simple operation and high extraction accuracy.However,when soil moisture and acquisition times vary,the extraction results are less accurate.To accommodate various conditions of FVC extraction,this study proposes a new FVC extraction method that extracts FVC from a normalized difference vegetation index(NDVI)greyscale image of wheat by using a density peak k-means(DPK-means)algorithm.In this study,Yangfumai 4(YF4)planted in pots and Yangmai 16(Y16)planted in the field were used as the research materials.With a hyperspectral imaging camera mounted on a tripod,ground hyperspectral images of winter wheat under different soil conditions(dry and wet)were collected at 1 m above the potted wheat canopy.Unmanned aerial vehicle(UAV)hyperspectral images of winter wheat at various stages were collected at 50 m above the field wheat canopy by a UAV equipped with a hyperspectral camera.The pixel dichotomy method and DPK-means algorithm were used to classify vegetation pixels and non-vegetation pixels in NDVI greyscale images of wheat,and the extraction effects of the two methods were compared and analysed.The results showed that extraction by pixel dichotomy was influenced by the acquisition conditions and its error distribution was relatively scattered,while the extraction effect of the DPK-means algorithm was less affected by the acquisition conditions and its error distribution was concentrated.The absolute values of error were 0.042 and 0.044,the root mean square errors(RMSE)were 0.028 and 0.030,and the fitting accuracy R2 of the FVC was 0.87 and 0.93,under dry and wet soil conditions and under various time conditions,respectively.This study found that the DPK-means algorithm was capable of achieving more accurate results than the pixel dichotomy method in various soil and time conditions and was an accurate and robust method for FVC extraction.

Polarimetric Meteorological Satellite Data Processing Software Classification Based on Principal Component Analysis and Improved K-Means Algorithm

With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In this paper, a set of software classification method based on software operating characteristics is proposed. The method uses software run-time resource consumption to describe the software running characteristics. Firstly, principal component analysis (PCA) is used to reduce the dimension of software running feature data and to interpret software characteristic information. Then the modified K-means algorithm was used to classify the meteorological data processing software. Finally, it combined with the results of principal component analysis to explain the significance of various types of integrated software operating characteristics. And it is used as the basis for optimizing the allocation of software hardware resources and improving the efficiency of software operation.

Hybrid Genetic Algorithm with K-Means for Clustering Problems

The K-means method is one of the most widely used clustering methods and has been implemented in many fields of science and technology. One of the major problems of the k-means algorithm is that it may produce empty clusters depending on initial center vectors. Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary principles of natural selection and genetics. This paper presents a hybrid version of the k-means algorithm with GAs that efficiently eliminates this empty cluster problem. Results of simulation experiments using several data sets prove our claim.

Development of slope mass rating system using K-means and fuzzy c-means clustering algorithms

Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.

Similarity matrix-based K-means algorithm for text clustering

K-means algorithm is one of the most widely used algorithms in the clustering analysis. To deal with the problem caused by the random selection of initial center points in the traditional al- gorithm, this paper proposes an improved K-means algorithm based on the similarity matrix. The im- proved algorithm can effectively avoid the random selection of initial center points, therefore it can provide effective initial points for clustering process, and reduce the fluctuation of clustering results which are resulted from initial points selections, thus a better clustering quality can be obtained. The experimental results also show that the F-measure of the improved K-means algorithm has been greatly improved and the clustering results are more stable.

A Hybrid Method Combining Improved K-means Algorithm with BADA Model for Generating Nominal Flight Profiles

A high-precision nominal flight profile,involving controllers′intentions is critical for 4Dtrajectory estimation in modern automatic air traffic control systems.We proposed a novel method to effectively improve the accuracy of the nominal flight profile,including the nominal altitude profile and the speed profile.First,considering the characteristics of trajectory data,we developed an improved K-means algorithm.The approach was to measure the similarity between different altitude profiles by integrating the space warp edit distance algorithm,thereby to acquire several fitted nominal flight altitude profiles.This approach breaks the constraints of traditional K-means algorithms.Second,to eliminate the influence of meteorological factors,we introduced historical gridded binary data to determine the en-route wind speed and temperature via inverse distance weighted interpolation.Finally,we facilitated the true airspeed determined by speed triangle relationships and the calibrated airspeed determined by aircraft data model to extract a more accurate nominal speed profile from each cluster,therefore we could describe the airspeed profiles above and below the airspeed transition altitude,respectively.Our experimental results showed that the proposed method could obtain a highly accurate nominal flight profile,which reflects the actual aircraft flight status.

An Improved K-Means Algorithm Based on Initial Clustering Center Optimization

The K-means algorithm is widely known for its simplicity and fastness in text clustering.However,the selection of the initial clus?tering center with the traditional K-means algorithm is some random,and therefore,the fluctuations and instability of the clustering results are strongly affected by the initial clustering center.This paper proposed an algorithm to select the initial clustering center to eliminate the uncertainty of central point selection.The experiment results show that the improved K-means clustering algorithm is superior to the traditional algorithm.

A State of Art Analysis of Telecommunication Data by k-Means and k-Medoids Clustering Algorithms

Cluster analysis is one of the major data analysis methods widely used for many practical applications in emerging areas of data mining. A good clustering method will produce high quality clusters with high intra-cluster similarity and low inter-cluster similarity. Clustering techniques are applied in different domains to predict future trends of available data and its uses for the real world. This research work is carried out to find the performance of two of the most delegated, partition based clustering algorithms namely k-Means and k-Medoids. A state of art analysis of these two algorithms is implemented and performance is analyzed based on their clustering result quality by means of its execution time and other components. Telecommunication data is the source data for this analysis. The connection oriented broadband data is given as input to find the clustering quality of the algorithms. Distance between the server locations and their connection is considered for clustering. Execution time for each algorithm is analyzed and the results are compared with one another. Results found in comparison study are satisfactory for the chosen application.

Multiple Parameter Based Clustering (MPC): Prospective Analysis for Effective Clustering in Wireless Sensor Network (WSN) Using K-Means Algorithm

In wireless sensor network cluster architecture is useful because of its inherent suitability for data fusion. In this paper we represent a new approach called Multiple Parameter based Clustering (MPC) embedded with the traditional k-means algorithm which takes different parameters (Node energy level, Euclidian distance from the base station, RSSI, Latency of data to reach base station) into consideration to form clusters. Then the effectiveness of the clusters is evaluated based on the uniformity of the node distribution, Node range per cluster, Intra and Inter cluster distance and required energy level of each centroid. Our result shows that by varying multiple parameters we can create clusters with more uniformly distributed nodes, minimize intra and maximize inter cluster distance and elect less power consuming centroid.

基于iForest+Biscting K-means的驾驶风格辨识方法研究

提出了一种基于iForest+Biscting K-means模型的客运驾驶员驾驶风格辨识方法。该方法针对在Bisceting Kmeans模型中,聚类质心严重影响聚类结果问题,采用iForest模型训练聚类中心候选集作为聚类质心集的方法加以改进。通过考察某城际客运线路30位客车职业驾驶员,在直线道路行驶工况下,90余天约400万条客车行驶数据开展模型验证。试验表明:在加速度标准差和超速倾向系数作为聚类指标的情况下,客运驾驶员驾驶风格聚类为谨慎型、普通型和激进型3类,其中谨慎型11人,普通型19人,激进型0人。

A Parallel Algorithm for Adaptive Local Refinement of Tetrahedral Meshes Using Bisection

Local mesh refinement is one of the key steps in the implementations of adaptive finite element methods. This paper presents a parallel algorithm for distributed memory parallel computers for adaptive local refinement of tetrahedral meshes using bisection. This algorithm is used in PHG, Parallel Hierarchical Grid Chttp：//lsec. cc. ac. cn/phg/）, a toolbox under active development for parallel adaptive finite element solutions of partial differential equations. The algorithm proposed is characterized by allowing simukaneous refinement of submeshes to arbitrary levels before synchronization between submeshes and without the need of a central coordinator process for managing new vertices. Using the concept of canonical refinement, a simple proof of the independence of the resulting mesh on the mesh partitioning is given, which is useful in better understanding the behaviour of the biseetioning refinement procedure.

An Approximation Algorithm Based on Seeding Algorithm for Fuzzy k-Means Problem with Penalties

As a classic NP-hard problem in machine learning and computational geometry,the k-means problem aims to partition the given dataset into k clusters according to the minimal squared Euclidean distance.Different from k-means problem and most of its variants,fuzzy k-means problem belongs to the soft clustering problem,where each given data point has relationship to every center point.Compared to fuzzy k-means problem,fuzzy k-means problem with penalties allows that some data points need not be clustered instead of being paid penalties.In this paper,we propose an O(αk In k)-approximation algorithm based on seeding algorithm for fuzzy k-means problem with penalties,whereαinvolves the ratio of the maximal penalty value to the minimal one.Furthermore,we implement numerical experiments to show the effectiveness of our algorithm.

基于二分K-means的测试用例集约简方法

测试用例集约简是软件测试中的重要研究问题之一,目的是以尽量少的测试用例达到测试目标。为此,提出一种新的测试用例集约简方法。应用二分K-means聚类算法对回归测试的测试用例集进行约简,以白盒测试的路径覆盖为准则,对每个测试用例进行量化,使每个用例变成一个点。以黑盒测试的功能需求数作为聚类数,在聚类结果的每一簇中,按照离中心点的距离进行排序,依次从每一簇中选择测试用例,直至满足所有测试需求,得到约简的测试用例集。实验结果表明,该方法能有效地减小测试用例集的规模,降低用例集检错率。

基于距离和密度的PBK-means算法

K-means算法初始中心点选择的随机性以及对噪声点的敏感性,使得聚类结果易陷入局部最优解,为获得最佳初始聚类中心,提出一种基于距离和密度的并行二分K-means算法。计算数据集的平均样本距离,根据数据点之间的距离计算数据的权重,选择最大权重数据点作为第一个中心点,小于平均样本距离的数据点不参加下一次聚类,将剩余数据点的权重与中心点距离相乘,选择值最大的数据点作为下一个中心点,得到两个中心点后按照距离对数据进行分配,将每个中心点代表的类分为两类后在每类上继续重复上述步骤。通过模仿细胞分裂的方法对数据进行切分,构建一棵满二叉树,当叶子结点数超过类别数k时停止聚类,合并叶子结点得到k个初始聚类中心执行K-means算法。在UCI公开数据集上进行测试,结果表明,对比传统K-means算法、Canopy-Kmeans算法、二分K-means算法、WK-means算法、MWK-means算法和DCK-means算法,该算法效率更高,具有较好的聚类效果。

KNOT POINT PLANNING FOR CARTESIAN TRAJECTORY GENERATION BASED ON INHERITANCE BISECTION ALGORITHM

The computation algorithm of knot point planning for Cartesian trajectorygeneration of manipulator is investigated, A novel inheritance bisection algorithm (IBA) based onconventional bisection algorithm (B A) is proposed. IBA has two steps. The first step is the 1 stknot point planning under lower set position accuracy; the second step is the 2nd knot pointplanning that inherits the results of the 1st planning under higher set position accuracy. Thesimulation results reveal that the number of inverse kinematical calculation (IKC) caused by IBA isdecreased compared with BA. IBA is more efficient to plan knot points.