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Journal articles on the topic 'Histogram Clustering'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Ambarwati, Ambarwati, and Edi Winarko. "Pengelompokan Berita Indonesia Berdasarkan Histogram Kata Menggunakan Self-Organizing Map." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 8, no. 1 (2014): 101. http://dx.doi.org/10.22146/ijccs.3500.

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AbstrakBerita merupakan sumber informasi yang dinantikan oleh manusia setiap harinya. Manusia membaca berita dengan kategori yang diinginkan. Jika komputer mampu mengelompokkan berita secara otomatis maka tentunya manusia akan lebih mudah membaca berita sesuai dengan kategori yang diinginkan. Pengelompokan berita yang berupa artikel secara otomatis sangatlah menarik karena mengorganisir artikel berita secara manual membutuhkan waktu dan biaya yang tidak sedikit.Tujuan penelitian ini adalah membuat sistem aplikasi untuk pengelompokkan artikel berita dengan menggunakan algoritma Self Organizing

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2

Billard, L., and Jaejik Kim. "Hierarchical clustering for histogram data." Wiley Interdisciplinary Reviews: Computational Statistics 9, no. 5 (2017): e1405. http://dx.doi.org/10.1002/wics.1405.

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Jing, Hui, Cong Li, Mei Fa Huang, and Fu Yun Liu. "A Fast Retrieval Method Based on K-Means Clustering for Mechanical Product Design." Advanced Materials Research 156-157 (October 2010): 98–101. http://dx.doi.org/10.4028/www.scientific.net/amr.156-157.98.

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Radius Angle Histogram (RAH) is useful method of retrieving 3D mechanical models. This method, however, not completely uses the information of radius angle histogram of the model. As a result, the retrieval precision is not very high enough. To improve the retrieval efficiency, K-means clustering method (KMM) is proposed in this paper. The radius angle histograms of the models are established first and then be served as inputs as KMM, respectively. By using KMM, the models can be classified and the results can be obtained. To validate the proposed method, an experiment is given. The results sh

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4

Sidorova, V. S. "Histogram clustering validation for multispectral image." Optoelectronics, Instrumentation and Data Processing 43, no. 1 (2007): 28–32. http://dx.doi.org/10.3103/s8756699007010049.

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5

Sengee, Nyamlkhagva, and Heung Kook Choi. "Brightness preserving weight clustering histogram equalization." IEEE Transactions on Consumer Electronics 54, no. 3 (2008): 1329–37. http://dx.doi.org/10.1109/tce.2008.4637624.

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6

Cho, Jae-Hyun. "Psychology Analysis using Color Histogram Clustering." Journal of the Korea institute of electronic communication sciences 8, no. 3 (2013): 415–20. http://dx.doi.org/10.13067/jkiecs.2013.8.3.415.

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Coşkun, Aysu, and Sándor Bilicz. "Data-Driven Clustering and Classification of Road Vehicle Radar Scattering Characteristics Using Histogram-Based RCS Features." Electronics 14, no. 4 (2025): 759. https://doi.org/10.3390/electronics14040759.

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This paper presents the clustering and classification of the radar scattering characteristics of vehicles under real-world driving conditions. The classification of 14 distinct vehicle types is achieved through statistical features derived from their radar cross-section (RCS) characteristics, represented as histograms. Various machine learning classification techniques are applied, and their performance is evaluated across different clustering scenarios. The results of the clustering algorithm are in line with the physics-based expectations on the scattering from different vehicle types. The c

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guojing, FENG Jun, YE Haosheng, and ZHOU Gang. "Retrieval-angle clustering histogram and clustering for 3D model retrieval." Journal of Image and Graphics 15, no. 11 (2010): 1644. http://dx.doi.org/10.11834/jig.20101101.

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9

Lan, Hong, and Shao Bin Jin. "An Improved Suppressed FCM Algorithm for Image Segmentation." Advanced Materials Research 712-715 (June 2013): 2349–53. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.2349.

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Fuzzy C-Means clustering(FCM) algorithm plays an important role in image segmentation, but it is sensitive to noise because of not taking into account the spatial information. Addressing this problem, this paper presents an improved suppressed FCM algorithm based on the pixels and the spatial neighborhood information of the image. The algorithm combines the two-dimentional histogram and suppressed FCM algorithm together. First, construct a two-dimentional histogram instead of one-dimentional histogram, which can better distinguish the distribution of the object and background for noisy images.

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Seo, Bo Bae, and Young Joo Yoon. "A study on sparse k-means clustering for histogram-valued data." Korean Data Analysis Society 26, no. 5 (2024): 1317–29. http://dx.doi.org/10.37727/jkdas.2024.26.5.1317.

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In this paper, we investigate a sparse k-means clustering method for histogram-valued data. The distances between histogram-valued observations are defined using the Wasserstein-Kantorovich distances to group p-dimensional histogram-valued data. Clustering is performed using the sparse k-means clustering method with the distance matrix computed for each dimension. The proposed method maximizes the weighted sums of squared distances between clusters. For various value of k, we apply the sparse k-means clustering method and determine the optimal number of clusters with the Silhouette measure. Si

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11

Stolk, Ad, Torbjörn E. Törnqvist, Kilian P. V. Hekhuis, Henk J. A. Berendsen, and Johannes van der Plicht. "Calibration of 14C Histograms: A Comparison of Methods." Radiocarbon 36, no. 1 (1994): 1–10. http://dx.doi.org/10.1017/s0033822200014272.

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The interpretation of 14C histograms is complicated by the non-linearity of the 14C time scale in terms of calendar years, which may result in clustering of 14C ages in certain time intervals unrelated to the (geologic or archaeologic) phenomenon of interest. One can calibrate 14C histograms for such distortions using two basic approaches. The KORHIS method constructs a 14C histogram before calibration is performed by means of a correction factor. We present the CALHIS method based on the Groningen calibration program for individual 14C ages. CALHIS first calibrates single 14C ages and then su

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12

Kim, Jaejik, and L. Billard. "Double monothetic clustering for histogram-valued data." Communications for Statistical Applications and Methods 25, no. 3 (2018): 263–74. http://dx.doi.org/10.29220/csam.2018.25.3.263.

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13

Park, Cheolwoo, Hosik Choi, Chris Delcher, Yanning Wang, and Young Joo Yoon. "Convex clustering analysis for histogram‐valued data." Biometrics 75, no. 2 (2019): 603–12. http://dx.doi.org/10.1111/biom.13004.

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Chen, Hai-peng, Xuan-Jing Shen, and Jian-Wu Long. "Histogram-based colour image fuzzy clustering algorithm." Multimedia Tools and Applications 75, no. 18 (2015): 11417–32. http://dx.doi.org/10.1007/s11042-015-2860-6.

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15

Balzanella, Antonio, and Rosanna Verde. "Histogram-based clustering of multiple data streams." Knowledge and Information Systems 62, no. 1 (2019): 203–38. http://dx.doi.org/10.1007/s10115-019-01350-5.

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16

Fushing, Hsieh, and Tania Roy. "Complexity of possibly gapped histogram and analysis of histogram." Royal Society Open Science 5, no. 2 (2018): 171026. http://dx.doi.org/10.1098/rsos.171026.

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We demonstrate that gaps and distributional patterns embedded within real-valued measurements are inseparable biological and mechanistic information contents of the system. Such patterns are discovered through data-driven possibly gapped histogram, which further leads to the geometry-based analysis of histogram (ANOHT). Constructing a possibly gapped histogram is a complex problem of statistical mechanics due to the ensemble of candidate histograms being captured by a two-layer Ising model. This construction is also a distinctive problem of Information Theory from the perspective of data compr

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He, Jin Guo. "A Novel Threshold Selection Method Based on Iterative Clustering Strategy." Applied Mechanics and Materials 433-435 (October 2013): 288–96. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.288.

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This paper extends our previous algorithm for clustering. This previous algorithm works fine on simulated data. It can acquire satisfactory clustering results even with annular or zonal simulated data by causing the data to shrink within a cluster. To make use of the advantages of the previous algorithm, a one-dimensional (1D) histogram is mapped to a two-dimensional (2D) image and can be clustered by the previous algorithm, thus leading to stable results of histogram thresholds. The shrinking procedures of the 2D image or the 1D histogram are given, and a new parameter strategy is discussed.

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Zhang, Tianjin, Zongrui Yi, Jinta Zheng, et al. "A Clustering-Based Automatic Transfer Function Design for Volume Visualization." Mathematical Problems in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/4547138.

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The two-dimensional transfer functions (TFs) designed based on intensity-gradient magnitude (IGM) histogram are effective tools for the visualization and exploration of 3D volume data. However, traditional design methods usually depend on multiple times of trial-and-error. We propose a novel method for the automatic generation of transfer functions by performing the affinity propagation (AP) clustering algorithm on the IGM histogram. Compared with previous clustering algorithms that were employed in volume visualization, the AP clustering algorithm has much faster convergence speed and can ach

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19

Zhao, Xi, Yun Zhang, Shoulie Xie, Qianqing Qin, Shiqian Wu, and Bin Luo. "Outlier Detection Based on Residual Histogram Preference for Geometric Multi-Model Fitting." Sensors 20, no. 11 (2020): 3037. http://dx.doi.org/10.3390/s20113037.

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Geometric model fitting is a fundamental issue in computer vision, and the fitting accuracy is affected by outliers. In order to eliminate the impact of the outliers, the inlier threshold or scale estimator is usually adopted. However, a single inlier threshold cannot satisfy multiple models in the data, and scale estimators with a certain noise distribution model work poorly in geometric model fitting. It can be observed that the residuals of outliers are big for all true models in the data, which makes the consensus of the outliers. Based on this observation, we propose a preference analysis

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SHAN, YING, HARPREET S. SAWHNEY, and ART POPE. "CLUSTERING MULTIPLE IMAGE SEQUENCES WITH A SEQUENCE-TO-SEQUENCE SIMILARITY MEASURE." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 04 (2005): 551–64. http://dx.doi.org/10.1142/s0218001405004149.

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We propose a novel similarity measure of two image sequences based on shapeme histograms. The idea of shapeme histogram has been used for single image/texture recognition, but is used here to solve the sequence-to-sequence matching problem. We develop techniques to represent each sequence as a set of shapeme histograms, which captures different variations of the object appearances within the sequence. These shapeme histograms are computed from the set of 2D invariant features that are stable across multiple images in the sequence, and therefore minimizes the effect of both background clutter,

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21

Gong, Xiwen, and Zilin Zhou. "Cluster finder for 1D and 2D histogram data." Applied and Computational Engineering 131, no. 1 (2025): 275–79. https://doi.org/10.54254/2755-2721/2024.20794.

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Clustering is a fundamental unsupervised task in machine learning. It involves grouping a set of objects into distinct clusters based on their inherent properties. Clustering finds applications in various domains, such as image segmentation, customer segmentation, document categorization, anomaly detection, and social network analysis. In this paper, we investigate several clustering algorithms applied to 1D and 2D histogram data. In particular, we try the Center of Gravity, Gaussian Mixture Model, and Neural Network and conclude that in low-dimensional cases, simple methods can yield good per

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22

Li, Ting Jun, You Ming Liu, Zhi Yu Che, Chang Wen Qu, and Yang Zhang. "Algorithm for TDOA Sorting Based on Clustering." Advanced Materials Research 1049-1050 (October 2014): 1308–11. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1308.

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Correctly sorting the staggered pulse trains each station received is one of the key technologies in the location effect of multi-station time difference passive detection system. According to the problem of straight grid division and the difficulty in sorting two emitters with the same one-dimensional time difference in the histogram method, a time difference sorting algorithm based on natural clustering is proposed. Simulation results show that the algorithm can overcome the defects of histogram method above, and solve the pulse miss-sorting problem, offering better sorting results.

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23

Chen, Aiguo, and Haoyuan Yan. "An Improved Fuzzy C-Means Clustering for Brain MR Images Segmentation." Journal of Medical Imaging and Health Informatics 11, no. 2 (2021): 386–90. http://dx.doi.org/10.1166/jmihi.2021.3296.

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In this paper, an improved fast FCM (HF-KFCM) algorithm was proposed based on histogram statistics of brain MR images. The algorithm firstly uses the multi-scale window traversal method to find the peak point of the histogram, then uses it as the initialization center of fuzzy clustering, and finally uses the fast clustering method based on statistical information to traverse, so as to reduce the computation amount of each iteration. Experimental results show that compared with the standard FCM algorithm and other improved algorithms, the proposed algorithm is significantly improved in cluster

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24

Costa, António C., J. A. Tenreiro Machado, and Maria Dulce Quelhas. "Multidimensional Scaling Applied to Histogram-Based DNA Analysis." Comparative and Functional Genomics 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/289694.

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This paper aims to study the relationships between chromosomal DNA sequences of twenty species. We propose a methodology combining DNA-based word frequency histograms, correlation methods, and an MDS technique to visualize structural information underlying chromosomes (CRs) and species. Four statistical measures are tested (Minkowski, Cosine, Pearson product-moment, and Kendallτrank correlations) to analyze the information content of 421 nuclear CRs from twenty species. The proposed methodology is built on mathematical tools and allows the analysis and visualization of very large amounts of st

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25

Nguyen, Chi, Thao Dinh, Van-Hau Nguyen, Nhat Tran, and Anh Le. "Histogram-based Feature Extraction for GPS Trajectory Clustering." EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 7, no. 22 (2020): 162796. http://dx.doi.org/10.4108/eai.13-7-2018.162796.

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26

Puzicha, Jan, Thomas Hofmann, and Joachim M. Buhmann. "Histogram clustering for unsupervised segmentation and image retrieval." Pattern Recognition Letters 20, no. 9 (1999): 899–909. http://dx.doi.org/10.1016/s0167-8655(99)00056-2.

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27

Chen, Ji, Kaiping Zhan, Qingzhou Li, et al. "Spectral clustering based on histogram of oriented gradient (HOG) of coal using laser-induced breakdown spectroscopy." Journal of Analytical Atomic Spectrometry 36, no. 6 (2021): 1297–305. http://dx.doi.org/10.1039/d1ja00104c.

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Histogram of oriented gradients (HOG) was introduced in the unsupervised spectral clustering in LIBS. After clustering, the spectra of different matrices were clearly distinguished, and the accuracy of quantitative analysis of coal was improved.

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Niemelä, Marko, Mikaela von Bonsdorff, Sami Äyrämö, and Tommi Kärkkäinen. "Classification of dementia from spoken speech using feature selection and the bag of acoustic words model." Applied Computing and Intelligence 4, no. 1 (2024): 45–65. http://dx.doi.org/10.3934/aci.2024004.

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<p>Memory disorders and dementia are a central factor in the decline of functioning and daily activities in older individuals. The workload related to standardized speech tests in clinical settings has led to a growing emphasis on developing automatic machine learning techniques for analyzing naturally spoken speech. This study presented a bag of acoustic words approach for distinguishing dementia patients from control individuals based on audio speech recordings. In this approach, each individual's speech was segmented into voiced periods, and these segments were characterized by acoust

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29

Yi, Zeguang, Nan Pan, Yi Liu, and Yu Guo. "Study of laser displacement measurement data abnormal correction algorithm." Engineering Computations 34, no. 1 (2017): 123–33. http://dx.doi.org/10.1108/ec-10-2015-0325.

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Purpose This paper aims to reduce and eliminate the abnormal peaks which, because of the reflection in the process of laser detection, make it easier to proceed with further analysis. Design/methodology/approach To solve the above problem, an abnormal data correction algorithm based on histogram, K-Means clustering and improved robust locally weighted scatter plot smoothing (LOWESS) is put forward. The proposed algorithm does section leveling for shear plant first and then applies histogram to define the abnormal fluctuation data between the neighboring points and utilizes a K-Means clustering

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Melnyk, Roman, and Andrii Shpek. "Defects Detection in PCB Images by Scanning Procedure, Flood-filling and Mathematical Comparison." WSEAS TRANSACTIONS ON CIRCUITS AND SYSTEMS 22 (December 31, 2023): 206–17. http://dx.doi.org/10.37394/23201.2023.22.23.

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The basis of the approach is a scanning procedure with the movement of windows on the printed circuit board to detect defects of various types. Mathematical image comparison, pixel distribution histograms, padding algorithms, statistical calculations, and histogram deviation measurements are applied to the small parts of the PCB image in a small window area. The paper considers K-mean clustering of pixel intensities to simplify the printed circuit board image, separation of elements on the printed circuit board image by filling with colors, determination of defect intensity, and subtraction fo

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XIE, WEIXIN, and JIANZHUANG LIU. "FUZZY C-MEANS CLUSTERING ALGORITHM WITH TWO LAYERS AND ITS APPLICATION TO IMAGE SEGMENTATION BASED ON TWO-DIMENSIONAL HISTOGRAM." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 02, no. 03 (1994): 343–50. http://dx.doi.org/10.1142/s0218488594000286.

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This paper presents a fast fuzzy c-means (FCM) clustering algorithm with two layers, which is a mergence of hard clustering and fuzzy clustering. The result of hard clustering is used to initialize the c cluster centers in fuzzy clustering, and then the number of iteration steps is reduced. The application of the proposed algorithm to image segmentation based on the two dimensional histogram is provided to show its computational efficience.

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Mozaffari, Mohammad Hamed, and Seyed Hamid Zahiri. "UNSUPERVISED DATA AND HISTOGRAM CLUSTERING USING INCLINED PLANES SYSTEM OPTIMIZATION ALGORITHM." Image Analysis & Stereology 33, no. 1 (2014): 65. http://dx.doi.org/10.5566/ias.v33.p65-74.

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Within the last decades, clustering has gained significant recognition as one of the data mining methods, especially in the relatively new field of medical engineering for diagnosing cancer. Clustering is used as a database to automatically group items with similar characteristics. Researchers aim to introduce a novel and powerful algorithm known as Inclined Planes system Optimization (IPO), with capacity to overcome clustering problems. The proposed method identifies each agent used in the algorithm to indicate the centroids of the clusters and automatically select the number of centroids in

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33

Nagarajan, Nagarani, and Sivasankari Jothiraj. "An Innovative Runway Landing Path Detection using UAV Implementation of the K-Means Clustering Algorithm." Indian Journal Of Science And Technology 17, no. 15 (2024): 1527–34. http://dx.doi.org/10.17485/ijst/v17i15.2495.

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Objective: To provide a novel approach for automatic Unmanned Aerial Vehicle (UAV) runway detection, leveraging remote sensing data and advanced image processing techniques. Methods: The methodology encompasses Gaussian filter-based despeckling and histogram equalization for preprocessing, followed by Independent Component Analysis (ICA) for feature extraction and segmentation using the K-means clustering algorithm. Findings: The research demonstrates successful UAV runway detection, even with unlabeled datasets, underscoring the efficacy of the proposed methods. Notably, the study contributes

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Roman, Melnyk, and Kvit Roman. "MEASUREMENT OF MATERIAL SURFACE DEFECT INTENSITY BY DISTRIBUTED CUMULATIVE HISTOGRAM AND CLUSTERING." TECHNOLOGY AUDIT AND PRODUCTION RESERVES 4, no. 2 (54) (2020): 36–45. https://doi.org/10.15587/2706-5448.2020.210151.

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The object of research is a distributed cumulative histogram of a digital image and its advantages for auto-mated determination of the location and intensity of defects of different nature on the surfaces of materials: metal, paper, etc. The technique considered in the study is aimed at minimization of human interference in the process of material surface control from the moment of its photographing to the moment of making a decision about the surface quality. Three-dimensional distributed cumulative histogram (DCH) is presented as a two-dimensional image in which the pixel intensity correspon

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Ke xin Jia, Miao He, Ting Cheng, Hui Yong Li, and Jun Li. "Multi-sphere Support Vector Clustering Based on Statistical Histogram." Journal of Convergence Information Technology 6, no. 9 (2011): 66–74. http://dx.doi.org/10.4156/jcit.vol6.issue9.8.

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36

Tsai, Du-Ming, and Ying-Hsiung Chen. "A fast histogram-clustering approach for multi-level thresholding." Pattern Recognition Letters 13, no. 4 (1992): 245–52. http://dx.doi.org/10.1016/0167-8655(92)90075-b.

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37

Roman, Melnyk, Tushnytskyy Ruslan, Kvit Roman, and Salo Tetyana. "PRELIMINARY DATA CLASSIFICATION BY MULTILEVEL SEGMENTATION OF HISTOGRAMS FOR CLUSTERING OF HYPERCUBES." TECHNOLOGY AUDIT AND PRODUCTION RESERVES 6, no. 2(56) (2020): 47–55. https://doi.org/10.15587/2706-5448.2020.220428.

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<em>The object of research is an algorithm for the classification of large data based on the hierarchical clustering algorithm. The nonlinear complexity of the clustering algorithm does not allow for data samples of 5–10 thousand and above.</em><em> </em><em>To classify data, it is necessary to pre-group them. Therefore, the subject of research is the data segmentation algorithm based on piecewise linear approximation.</em> <em>In the course of the study, let’s use hierarchical clustering algorithms, the method of piecewise</em><em> </em><em>linear approximation of the cu

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Rasras, Rashad J., Bilal Zahran, Mutaz Rasmi Abu Sara, and Ziad AlQadi. "Developing digital signal clustering method using local binary pattern histogram." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 872. http://dx.doi.org/10.11591/ijece.v11i1.pp872-878.

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In this paper we presented a new approach to manipulate a digital signal in order to create a features array, which can be used as a signature to retrieve the signal. Each digital signal is associated with the local binary pattern (LBP) histogram; this histogram will be calculated based on LBP operator, then k-means clustering was used to generate the required features for each digital signal. The proposed method was implemented, tested and the obtained experimental results were analyzed. The results showed the flexibility and accuracy of the proposed method. Althoug different parameters of th

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Rashad, J. Rasras, Zahran Bilal, Rasmi Abu Sara Mutaz, and AlQadi Ziad. "Developing digital signal clustering method using local binary pattern histogram." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 872–78. https://doi.org/10.11591/ijece.v11i1.pp872-878.

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In this paper we presented a new approach to manipulate a digital signal in order to create a features array, which can be used as a signature to retrieve the signal. Each digital signal is associated with the local binary pattern (LBP) histogram; this histogram will be calculated based on LBP operator, then k-means clustering was used to generate the required features for each digital signal. The proposed method was implemented, tested and the obtained experimental results were analyzed. The results showed the flexibility and accuracy of the proposed method. Althoug different parameters of th

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Buhmann, Joachim M., Tilman Lange, and Ulrich Ramacher. "Image Segmentation by Networks of Spiking Neurons." Neural Computation 17, no. 5 (2005): 1010–31. http://dx.doi.org/10.1162/0899766053491913.

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A network of leaky integrate-and-fire (IAF) neurons is proposed to segment gray-scale images. The network architecture with local competition between neurons that encode segment assignments of image blocks is motivated by a histogram clustering approach to image segmentation. Lateral excitatory connections between neighboring image sites yield a local smoothing of segments. The mean firing rate of class membership neurons encodes the image segmentation. A weight modification scheme is proposed that estimates segment-specific prototypical histograms. The robustness properties of the network imp

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Zhao, Hong, Wei-Jie Wang, Tao Wang, Zhao-Bin Chang, and Xiang-Yan Zeng. "Key-Frame Extraction Based on HSV Histogram and Adaptive Clustering." Mathematical Problems in Engineering 2019 (September 22, 2019): 1–10. http://dx.doi.org/10.1155/2019/5217961.

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Along with the fast development of digital information technology and the application of Internet, video data begins to grow explosively. Some applications with high real-time requirements, such as object detection, require strong online video storage and analysis capabilities. Key-frame extraction is an important technique in video analysis, which provides an organizational framework for dealing with video content and reduces the amount of data required in video indexing. To address the problem, this study proposes a key-frame extraction method based on HSV (hue, saturation, value) histogram

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Jia, Qi, Xu Liang Lü, Wei Dong Xu, Jiang Hua Hu, and Xian Hui Rong. "Comparison of Rapid Extraction Algorithms of Major Colors." Applied Mechanics and Materials 441 (December 2013): 687–90. http://dx.doi.org/10.4028/www.scientific.net/amm.441.687.

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The extraction of major colors is the basis of image processing. In the process of camouflage pattern painting design, whether major colors are extracted rapidly and precisely are very important to the application. Histogram clustering and K-means clustering and ISODATA clustering are three widely using extraction algorithms. To test and compare the accuracy of the three algorithms, the definition of average color difference is introduced. Then, two representative forest land background images are used to compare the performance of three algorithms. The result shows that, ISODATA clustering al

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Kumar, Ajay, Shishir Kumar, and Sakshi Saxena. "An Efficient Approach for Incremental Association Rule Mining through Histogram Matching Technique." International Journal of Information Retrieval Research 2, no. 2 (2012): 29–42. http://dx.doi.org/10.4018/ijirr.2012040103.

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The objective of the work being presented is to propose an approach for obtaining appropriate association rules when the data set is being incrementally updated. During this process raw data is clustered by K-mean Clustering Algorithm and appropriate rules are generated for each cluster. Further, a histogram and probability density function are also generated for each cluster. When Burst data set is coming to the system, initially the histogram and probability density function of this new data set are obtained. The new data set has to be added to the cluster whose histogram and probability den

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Inbarani H., Hannah, Ahmad Taher Azar, and Jothi G. "Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm." Electronics 9, no. 1 (2020): 188. http://dx.doi.org/10.3390/electronics9010188.

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Segmenting an image of a nucleus is one of the most essential tasks in a leukemia diagnostic system. Accurate and rapid segmentation methods help the physicians identify the diseases and provide better treatment at the appropriate time. Recently, hybrid clustering algorithms have started being widely used for image segmentation in medical image processing. In this article, a novel hybrid histogram-based soft covering rough k-means clustering (HSCRKM) algorithm for leukemia nucleus image segmentation is discussed. This algorithm combines the strengths of a soft covering rough set and rough k-me

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Nagarani, Nagarajan, and Jothiraj Sivasankari. "An Innovative Runway Landing Path Detection using UAV Implementation of the K-Means Clustering Algorithm." Indian Journal of Science and Technology 17, no. 15 (2024): 1527–34. https://doi.org/10.17485/IJST/v17i15.2495.

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Abstract <strong>Objective:</strong> To provide a novel approach for automatic Unmanned Aerial Vehicle (UAV) runway detection, leveraging remote sensing data and advanced image processing techniques. <strong>Methods:</strong> The methodology encompasses Gaussian filter-based despeckling and histogram equalization for preprocessing, followed by Independent Component Analysis (ICA) for feature extraction and segmentation using the K-means clustering algorithm. <strong>Findings:</strong> The research demonstrates successful UAV runway detection, even with unlabeled datase

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Li, Yahui, Natakorn Sapermsap, Jun Yu, Jinshou Tian, Yu Chen, and David Day-Uei Li. "Histogram clustering for rapid time-domain fluorescence lifetime image analysis." Biomedical Optics Express 12, no. 7 (2021): 4293. http://dx.doi.org/10.1364/boe.427532.

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Lo, Chi-Chun, and Shuenn-Jyi Wang. "A histogram-based moment-preserving clustering algorithm for video segmentation." Pattern Recognition Letters 24, no. 14 (2003): 2209–18. http://dx.doi.org/10.1016/s0167-8655(03)00048-5.

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An, Lingling, Xinbo Gao, Yuan Yuan, and Dacheng Tao. "Robust lossless data hiding using clustering and statistical quantity histogram." Neurocomputing 77, no. 1 (2012): 1–11. http://dx.doi.org/10.1016/j.neucom.2011.06.012.

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Ichino, Manabu, Kadri Umbleja, and Hiroyuki Yaguchi. "Unsupervised Feature Selection for Histogram-Valued Symbolic Data Using Hierarchical Conceptual Clustering." Stats 4, no. 2 (2021): 359–84. http://dx.doi.org/10.3390/stats4020024.

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This paper presents an unsupervised feature selection method for multi-dimensional histogram-valued data. We define a multi-role measure, called the compactness, based on the concept size of given objects and/or clusters described using a fixed number of equal probability bin-rectangles. In each step of clustering, we agglomerate objects and/or clusters so as to minimize the compactness for the generated cluster. This means that the compactness plays the role of a similarity measure between objects and/or clusters to be merged. Minimizing the compactness is equivalent to maximizing the dis-sim

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Zhang, Tai Fa. "An Improved C-Means Clustering for Image Segmentation." Advanced Materials Research 981 (July 2014): 344–47. http://dx.doi.org/10.4028/www.scientific.net/amr.981.344.

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Image segmentation is an important part of the image process, and it is also the current hot and focus in image research. How to achieve better segmentation results are dominating targets of researchers. Currently, image segmentation based on clustering is the main research area. Firstly, this paper introduces the traditional C-means clustering algorithm and its characteristic has been analyzed. Then, the initial clustering center and the number are selected using the histogram. Finally, the image is converted from the RGB space to Lab space for clustering, and it has improved the accuracy and

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