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Journal articles on the topic 'LINEAR ITERATIVE CLUSTERING'

Author: Grafiati

Published: 11 September 2023

Last updated: 31 July 2025

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1

Zhao, Jiaxing, Ren Bo, Qibin Hou, Ming-Ming Cheng, and Paul Rosin. "FLIC: Fast linear iterative clustering with active search." Computational Visual Media 4, no. 4 (2018): 333–48. http://dx.doi.org/10.1007/s41095-018-0123-y.

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2

Yan, Qingan, Long Yang, Chao Liang, Huajun Liu, Ruimin Hu, and Chunxia Xiao. "Geometrically Based Linear Iterative Clustering for Quantitative Feature Correspondence." Computer Graphics Forum 35, no. 7 (2016): 1–10. http://dx.doi.org/10.1111/cgf.12998.

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3

Magaraja, Anousouya Devi, Ezhilarasie Rajapackiyam, Vaitheki Kanagaraj, et al. "A Hybrid Linear Iterative Clustering and Bayes Classification-Based GrabCut Segmentation Scheme for Dynamic Detection of Cervical Cancer." Applied Sciences 12, no. 20 (2022): 10522. http://dx.doi.org/10.3390/app122010522.

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Cervical cancer earlier detection remains indispensable for enhancing the survival rate probability among women patients worldwide. The early detection of cervical cancer is done relatively by using the Pap Smear cell Test. This method of detection is challenged by the degradation phenomenon within the image segmentation task that arises when the superpixel count is minimized. This paper introduces a Hybrid Linear Iterative Clustering and Bayes classification-based GrabCut Segmentation Technique (HLC-BC-GCST) for the dynamic detection of Cervical cancer. In this proposed HLC-BC-GCST approach,

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4

Yamamoto, Takeshi, Katsuhiro Honda, Akira Notsu, and Hidetomo Ichihashi. "A Comparative Study on TIBA Imputation Methods in FCMdd-Based Linear Clustering with Relational Data." Advances in Fuzzy Systems 2011 (2011): 1–10. http://dx.doi.org/10.1155/2011/265170.

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Relational fuzzy clustering has been developed for extracting intrinsic cluster structures of relational data and was extended to a linear fuzzy clustering model based on Fuzzyc-Medoids (FCMdd) concept, in which Fuzzyc-Means-(FCM-) like iterative algorithm was performed by defining linear cluster prototypes using two representative medoids for each line prototype. In this paper, the FCMdd-type linear clustering model is further modified in order to handle incomplete data including missing values, and the applicability of several imputation methods is compared. In several numerical experiments,

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5

Eun, Hyunjun, Yoonhyung Kim, Chanho Jung, and Changick Kim. "Adaptive Sampling of Initial Cluster Centers for Simple Linear Iterative Clustering." Journal of Korean Institute of Communications and Information Sciences 43, no. 1 (2018): 20–23. http://dx.doi.org/10.7840/kics.2018.43.1.20.

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6

Oh, Ki-Won, and Kang-Sun Choi. "Acceleration of simple linear iterative clustering using early candidate cluster exclusion." Journal of Real-Time Image Processing 16, no. 4 (2016): 945–56. http://dx.doi.org/10.1007/s11554-016-0583-1.

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7

Choi, Kang-Sun, and Ki-Won Oh. "Subsampling-based acceleration of simple linear iterative clustering for superpixel segmentation." Computer Vision and Image Understanding 146 (May 2016): 1–8. http://dx.doi.org/10.1016/j.cviu.2016.02.018.

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8

Eremeev, S. V. "Clustering of spatial data with implicit polygonal structure based on topological approaches." Scientific and Technical Journal of Information Technologies, Mechanics and Optics 25, no. 2 (2025): 261–72. https://doi.org/10.17586/2226-1494-2025-25-2-261-272.

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Clustering is one of the fundamental approaches for data mining, which in the field of geoinformatics and image processing is used to search for knowledge and hidden patterns of spatial information. During automatic vectorization of objects on satellite images due to imperfections of these technologies, missing elements appear on linear and polygonal objects, which prevent full-fledged data analysis and visualization. The paper considers the problem of clustering geometric primitives with implicit polygonal structure with the possibility of eliminating incomplete data in vector models. The pro

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9

Huang, Hui-Yu, and Zhe-Hao Liu. "Stereo Matching with Spatiotemporal Disparity Refinement Using Simple Linear Iterative Clustering Segmentation." Electronics 10, no. 6 (2021): 717. http://dx.doi.org/10.3390/electronics10060717.

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Stereo matching is a challenging problem, especially for computer vision, e.g., three-dimensional television (3DTV) or 3D visualization. The disparity maps from the video streams must be estimated. However, the estimated disparity sequences may cause undesirable flickering errors. These errors result in poor visual quality for the synthesized video and reduce the video coding information. In order to solve this problem, we here propose a spatiotemporal disparity refinement method for local stereo matching using the simple linear iterative clustering (SLIC) segmentation strategy, outlier detect

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10

Cong, Jinyu, Benzheng Wei, Yilong Yin, Xiaoming Xi, and Yuanjie Zheng. "Performance evaluation of simple linear iterative clustering algorithm on medical image processing." Bio-Medical Materials and Engineering 24, no. 6 (2014): 3231–38. http://dx.doi.org/10.3233/bme-141145.

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11

Meenalochani, Manickam, Natarajan Hemavathi, and Selvaraj Sudha. "Performance analysis of iterative linear regression-based clustering in wireless sensor networks." IET Science, Measurement & Technology 14, no. 4 (2020): 423–29. http://dx.doi.org/10.1049/iet-smt.2019.0258.

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12

Tang, Xiaoqing, Junlong Chen, Yazhou Liu, and Quansen Sun. "Hyperspectral image classification by fusing sparse representation and simple linear iterative clustering." Journal of Applied Remote Sensing 9, no. 1 (2015): 095977. http://dx.doi.org/10.1117/1.jrs.9.095977.

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13

Marleny, Finki Dona, Ihdalhubbi Maulida, and Mambang Mambang. "SIMPLE LINEAR ITERATIVE CLUSTERING (SLIC) UNTUK SEGMENTASI MOTIF DASAR CITRA KAIN SASIRANGAN." Jurnal Simantec 11, no. 1 (2022): 19–26. http://dx.doi.org/10.21107/simantec.v11i1.14274.

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14

Zhu, Yaguang, Kailu Luo, Chao Ma, Qiong Liu, and Bo Jin. "Superpixel Segmentation Based Synthetic Classifications with Clear Boundary Information for a Legged Robot." Sensors 18, no. 9 (2018): 2808. http://dx.doi.org/10.3390/s18092808.

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In view of terrain classification of the autonomous multi-legged walking robots, two synthetic classification methods for terrain classification, Simple Linear Iterative Clustering based Support Vector Machine (SLIC-SVM) and Simple Linear Iterative Clustering based SegNet (SLIC-SegNet), are proposed. SLIC-SVM is proposed to solve the problem that the SVM can only output a single terrain label and fails to identify the mixed terrain. The SLIC-SegNet single-input multi-output terrain classification model is derived to improve the applicability of the terrain classifier. Since terrain classificat

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15

Nagata, Munehiro, Masatsugu Hada, Masashi Iwasaki, and Yoshimasa Nakamura. "Eigenvalue clustering of coefficient matrices in the iterative stride reductions for linear systems." Computers & Mathematics with Applications 71, no. 1 (2016): 349–55. http://dx.doi.org/10.1016/j.camwa.2015.11.022.

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16

Bommisetty, Reddy Mounika, Om Prakash, and Ashish Khare. "Video superpixels generation through integration of curvelet transform and simple linear iterative clustering." Multimedia Tools and Applications 78, no. 17 (2019): 25185–219. http://dx.doi.org/10.1007/s11042-019-7554-z.

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17

Liu, Tianli, Dongsong Li, Zhiming Jiao, Tao Liang, Hao Zhou, and Guoqing Yang. "A coloured oil level indicator detection method based on simple linear iterative clustering." IOP Conference Series: Earth and Environmental Science 100 (December 2017): 012151. http://dx.doi.org/10.1088/1755-1315/100/1/012151.

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18

Wang, Yuchan, Baojiu Li, and Marius Cautun. "Iterative removal of redshift-space distortions from galaxy clustering." Monthly Notices of the Royal Astronomical Society 497, no. 3 (2020): 3451–71. http://dx.doi.org/10.1093/mnras/staa2136.

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ABSTRACT Observations of galaxy clustering are made in redshift space, which results in distortions to the underlying isotropic distribution of galaxies. These redshift-space distortions (RSDs) not only degrade important features of the matter density field, such as the baryonic acoustic oscillation (BAO) peaks, but also pose challenges for the theoretical modelling of observational probes. Here, we introduce an iterative non-linear reconstruction algorithm to remove RSD effects from galaxy clustering measurements, and assess its performance by using mock galaxy catalogues. The new method is f

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19

Wuttke, S., W. Middelmann, and U. Stilla. "IMPROVING ACTIVE QUERIES WITH A LOCAL SEGMENTATION STEP AND APPLICATION TO LAND COVER CLASSIFICATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-1/W1 (May 30, 2017): 165–73. http://dx.doi.org/10.5194/isprs-annals-iv-1-w1-165-2017.

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Active queries is an active learning method used for classification of remote sensing images. It consists of three steps: hierarchical clustering, dendrogram division, and active label selection. The goal of active learning is to reduce the needed amount of labeled data while preserving classification accuracy. We propose to apply local segmentation as a new step preceding the hierarchical clustering. We are using the SLIC (simple linear iterative clustering) algorithm for dedicated image segmentation. This incorporates spatial knowledge which leads to an increased learning rate and reduces cl

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20

Prasad Kondisetty, Durga, and Mohammed Ali Hussain. "A novel approach for cDNA image segmentation using SLIC based SOM methodology." International Journal of Engineering & Technology 7, no. 2.8 (2018): 52. http://dx.doi.org/10.14419/ijet.v7i2.8.10323.

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In the segmentation of computer vision images, Super pixels are act as key role from last decade. There are multiple algorithms and techniques to analyze the Super pixels but amount all of them the best super pixel analyzing method is Simple Linear Iterative Clustering (SLIC) have come to pivot increasingly in recent years. The studying of micro array gene expression from MRI imaging is more useful to detect tumors or any other cancer diseases, so that the complementary DNA (cDNA) microarray is a well established tool for studying the same. The segmentation of microarray images is the main ste

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21

Setiawan, Alexander, Amadea Sapphira, and Endang Setyati. "Koi varieties identification based zero parameter simple linear iterative clustering and support vector machine." IOP Conference Series: Earth and Environmental Science 1445, no. 1 (2025): 012122. https://doi.org/10.1088/1755-1315/1445/1/012122.

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Abstract There’s currently 120 types of koi fish that has been bred around the world. The types of koi fish depend on the colour patterns and shapes they have. There’s alot of patterns that has similarity between one type with another. For example, sanke and showa koi fish will look similar from a non-expert’s point of view, because both type has same colour pattern, which is red, black and white. In actuality, sanke koi is dominantly red and white with slight black accent, while showa’s dominant colour is red and black, with white accent. In this research, Zero Parameter Simple Linear Iterati

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22

Angulakshmi, M., and G. G. Lakshmi Priya. "Walsh Hadamard Transform for Simple Linear Iterative Clustering (SLIC) Superpixel Based Spectral Clustering of Multimodal MRI Brain Tumor Segmentation." IRBM 40, no. 5 (2019): 253–62. http://dx.doi.org/10.1016/j.irbm.2019.04.005.

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23

Snehalatha, Snehalatha. "Brain Mri Image Segmentation Using Simple Linear Iterative Clustering (SLIC) Segmentation With Superpixel Fusion." Bioscience Biotechnology Research Communications 14, no. 5 (2021): 358–64. http://dx.doi.org/10.21786/bbrc/14.5/62.

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24

Liu, Bowen, Ting Zhang, Yujian Li, Zhaoying Liu, and Zhilin Zhang. "Kernel Probabilistic K-Means Clustering." Sensors 21, no. 5 (2021): 1892. http://dx.doi.org/10.3390/s21051892.

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Kernel fuzzy c-means (KFCM) is a significantly improved version of fuzzy c-means (FCM) for processing linearly inseparable datasets. However, for fuzzification parameter m=1, the problem of KFCM (kernel fuzzy c-means) cannot be solved by Lagrangian optimization. To solve this problem, an equivalent model, called kernel probabilistic k-means (KPKM), is proposed here. The novel model relates KFCM to kernel k-means (KKM) in a unified mathematic framework. Moreover, the proposed KPKM can be addressed by the active gradient projection (AGP) method, which is a nonlinear programming technique with co

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25

Tang, Wei, Yang Yang, Lanling Zeng, and Yongzhao Zhan. "Optimizing MSE for Clustering with Balanced Size Constraints." Symmetry 11, no. 3 (2019): 338. http://dx.doi.org/10.3390/sym11030338.

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Clustering is to group data so that the observations in the same group are more similar to each other than to those in other groups. k-means is a popular clustering algorithm in data mining. Its objective is to optimize the mean squared error (MSE). The traditional k-means algorithm is not suitable for applications where the sizes of clusters need to be balanced. Given n observations, our objective is to optimize the MSE under the constraint that the observations need to be evenly divided into k clusters. In this paper, we propose an iterative method for the task of clustering with balanced si

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26

Durga, Prasad Kondisetty, and Ali Hussain Mohammed. "SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Microarray Images." International Journal of Advances in Applied Sciences (IJAAS) 7, no. 1 (2018): 78–85. https://doi.org/10.11591/ijaas.v7.i1.pp78-85.

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We can find the simultaneous monitoring of thousands of genes in parallel Microarray technology. As per these measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, Intensity extraction, Enhancement and Segmentation are important steps in microarray image analysis. This paper gives simple linear iterative clustering (SLIC) based self organizing maps (SOM) algorithm for segmentation of microarray image. The clusters of pixels which share similar features are called Superpixel

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27

NARAZAKI, HIROSHI, and ANCA L. RALESCU. "ITERATIVE INDUCTION OF A CATEGORY MEMBERSHIP FUNCTION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 02, no. 01 (1994): 91–100. http://dx.doi.org/10.1142/s0218488594000080.

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We propose a new iterative method for inducing classification knowledge from symbolic data. Our method generates a hierarchy of clusters and does not assume a particular knowledge representation formula (e.g., a conjunctive formula, a linear discriminant function). Our method consists of two stages, i.e., the optimization and clustering stages. The first stage maps the symbolic problem into the numerical domain based on an optimization approach. In the second stage, the examples are clustered into positive, negative, and fuzzy zones using induced membership degrees. This learning procedure is

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28

Farmaha, Ihor, Marian Banaś, Vasyl Savchyn, Bohdan Lukashchuk, and Taras Farmaha. "Wound image segmentation using clustering based algorithms." New Trends in Production Engineering 2, no. 1 (2019): 570–78. http://dx.doi.org/10.2478/ntpe-2019-0062.

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Abstract Classic methods of measurement and analysis of the wounds on the images are very time consuming and inaccurate. Automation of this process will improve measurement accuracy and speed up the process. Research is aimed to create an algorithm based on machine learning for automated segmentation based on clustering algorithms Methods. Algorithms used: SLIC (Simple Linear Iterative Clustering), Deep Embedded Clustering (that is based on artificial neural networks and k-means). Because of insufficient amount of labeled data, classification with artificial neural networks can't reach good re

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29

P, Thamilselvan. "IMPROVING MEDICAL IMAGE PREPROCESSING USING DENOISING TECHNIQUE." ICTACT Journal on Image and Video Processing 12, no. 3 (2022): 2650–54. http://dx.doi.org/10.21917/ijivp.2022.0376.

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Image denoising is a main issue found in medical images and computer vision issues. There are different existing techniques in denoising image but the significant property of a decent image denoising model is that eliminate noise beyond what many would consider possible just as protect edges. Digital images accept a fundamental part both in step-by-step medical image applications, for instance, satellite TV, figured tomography. This method implemented for removing the noise from the lung cancer medical images with securing, transmission and gathering and capacity and recovery measures. This pa

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30

Ren Xinlei, 任欣磊, and 王阳萍 Wang Yangping. "Super-Pixel Segmentation of Remote Sensing Image Based on Improved Simple Linear Iterative Clustering Algorithm." Laser & Optoelectronics Progress 57, no. 22 (2020): 222801. http://dx.doi.org/10.3788/lop57.222801.

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31

Ren, Dayong, Zhenhong Jia, Jie Yang, and Nikola K. Kasabov. "A Practical GrabCut Color Image Segmentation Based on Bayes Classification and Simple Linear Iterative Clustering." IEEE Access 5 (2017): 18480–87. http://dx.doi.org/10.1109/access.2017.2752221.

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32

Kim, Yong Hwi, and Kwan H. Lee. "Data Driven SVBRDF Estimation Using Deep Embedded Clustering." Electronics 11, no. 19 (2022): 3239. http://dx.doi.org/10.3390/electronics11193239.

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Photo-realistic representation in user-specified view and lighting conditions is a challenging but high-demand technology in the digital transformation of cultural heritages. Despite recent advances in neural renderings, it is still necessary to capture high-quality surface reflectance from photography in a controlled environment for real-time applications such as VR/AR and digital arts. In this paper, we present a deep embedding clustering network for spatially-varying bidirectional reflectance distribution function (SVBRDF) estimation. Our network is designed to simultaneously update the ref

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33

Hermawan, Andy, Ilham Zaeni, Aji Wibawa, Gunawan Gunawan, Yosi Kristian, and Shandy Darmawan. "Pengenalan Varietas Ikan Koi Berdasarkan Foto Menggunakan Simple Linear Iterative Clustering Superpixel Segmentation dan Convolutional Neural." Jurnal Inovasi Teknologi dan Edukasi Teknik 1, no. 11 (2021): 806–14. http://dx.doi.org/10.17977/um068v1i112021p806-814.

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Object segmentation and image recognition are two computer vision tasks which are still being developed until today. Simple Linear Iterative Clustering is an algorithm which is very popular to help with object segmentation tasks because it is the best in terms of result and speed. In image recognition, Convolutional Neural Networks are also one of the best approaches for any kind of recognition tasks because of their efficiency and the ability to recognize objects like animals do. Koi fish have become a very interesting object to be researched because they are difficult to segment and distingu

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34

ZHANG, CHONG, XUANJING SHEN, and HAIPENG CHEN. "BRAIN TUMOR SEGMENTATION BASED ON SUPERPIXELS AND HYBRID CLUSTERING WITH FAST GUIDED FILTER." Journal of Mechanics in Medicine and Biology 20, no. 06 (2020): 2050032. http://dx.doi.org/10.1142/s0219519420500323.

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Brain tumor segmentation from magnetic resonance (MR) image is vital for both the diagnosis and treatment of brain cancers. To alleviate noise sensitivity and improve stability of segmentation, an effective hybrid clustering algorithm combined with fast guided filter is proposed for brain tumor segmentation in this paper. Preprocessing is performed using adaptive Wiener filtering combined with a fast guided filter. Then simple linear iterative clustering (SLIC) is utilized for pre-segmentation to effectively remove scatter. During the clustering, K-means[Formula: see text] and Gaussian kernel-

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35

DIMAGGIO, PETER A., SCOTT R. MCALLISTER, CHRISTODOULOS A. FLOUDAS, XIAO-JIANG FENG, JOSHUA D. RABINOWITZ, and HERSCHEL A. RABITZ. "OPTIMAL METHODS FOR RE-ORDERING DATA MATRICES IN SYSTEMS BIOLOGY AND DRUG DISCOVERY APPLICATIONS." Biophysical Reviews and Letters 03, no. 01n02 (2008): 19–42. http://dx.doi.org/10.1142/s1793048008000605.

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The analysis of large-scale data sets via clustering techniques is utilized in a number of applications. Many of the methods developed employ local search or heuristic strategies for identifying the "best" arrangement of features according to some metric. In this article, we present rigorous clustering methods based on the optimal re-ordering of data matrices. Distinct mixed-integer linear programming (MILP) models are utilized for the clustering of (a) dense data matrices, such as gene expression data, and (b) sparse data matrices, which are commonly encountered in the field of drug discovery

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36

Liu, Yiting, Lianjie Sui, Peijuan Li, et al. "A Radar Linear Feature Fitting Algorithm Combining Adaptive Clustering and Corner Detection Operator." Journal of Sensors 2023 (February 24, 2023): 1–17. http://dx.doi.org/10.1155/2023/6991467.

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The precise environmental parameters derived from laser radar scan data can significantly accelerate the process of real-time localization and map-matching technique. One of the research directions is autonomous navigation algorithm based on LiDAR slam. LiDAR has the advantage of having a wide range of accuracy and distance. However, due to the limited amount of LiDAR data available and the influence of sensor noise, it is easy to run into issues such as low accuracy of robot map construction or large positioning errors. At the moment, most of feature extraction algorithms employ an iterative

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37

Chang, Kaiwen, and Bruno Figliuzzi. "Fast marching based superpixels." Mathematical Morphology - Theory and Applications 4, no. 1 (2020): 127–42. http://dx.doi.org/10.1515/mathm-2020-0105.

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AbstractIn this article, we present a fast-marching based algorithm for generating superpixel (FMS) partitions of images. The idea behind the algorithm is to draw an analogy between waves propagating in a heterogeneous medium and regions growing on an image at a rate depending on the local color and texture. The FMS algorithm is evaluated on the Berkeley Segmentation Dataset 500. It yields results in terms of boundary adherence that are slightly better than the ones obtained with similar approaches including the Simple Linear Iterative Clustering, the Eikonal-based region growing for efficient

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38

Kondisetty, Durga Prasad, and Mohammed Ali Hussain. "SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Microarray Images." International Journal of Advances in Applied Sciences 7, no. 1 (2018): 78. http://dx.doi.org/10.11591/ijaas.v7.i1.pp78-85.

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We can find the simultaneous monitoring of thousands of genes in parallel Microarray technology. As per these measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, Intensity extraction, Enhancement and Segmentation are important steps in microarray image analysis. This paper gives simple linear iterative clustering (SLIC) based self organizing maps (SOM) algorithm for segmentation of microarray image. The clusters of pixels which share similar features are called Superpixel

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39

Lee, Jeong Hwan. "A Comparison of Superpixel Characteristics based on SLIC(Simple Linear Iterative Clustering) for Color Feature Spaces." Journal of the Korea Society of Digital Industry and Information Management 10, no. 4 (2014): 151–60. http://dx.doi.org/10.17662/ksdim.2014.10.1.151.

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40

Gopalakrishnan, Vithya. "Enhancement of Sales promotion using Clustering Techniques in Data Mart." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 2 (2015): 6534–40. http://dx.doi.org/10.24297/ijct.v15i2.6934.

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Clustering is an important research topic in wide range of unsupervised classification application. Clustering is a technique, which divides a data into meaningful groups. K-means algorithm is one of the popular clustering algorithms. It belongs to partition based grouping techniques, which are based on the iterative relocation of data points between clusters. It does not support global clustering and it has linear time complexity of O(n2). The existing and conventional data clustering algorithms were nâ€™t designed to handle the huge amount of data. So, to overcome these issues Golay code clu

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41

Liu, Xinwang, Xinzhong Zhu, Miaomiao Li, et al. "Efficient and Effective Incomplete Multi-View Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4392–99. http://dx.doi.org/10.1609/aaai.v33i01.33014392.

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Incomplete multi-view clustering (IMVC) optimally fuses multiple pre-specified incomplete views to improve clustering performance. Among various excellent solutions, the recently proposed multiple kernel k-means with incomplete kernels (MKKM-IK) forms a benchmark, which redefines IMVC as a joint optimization problem where the clustering and kernel matrix imputation tasks are alternately performed until convergence. Though demonstrating promising performance in various applications, we observe that the manner of kernel matrix imputation in MKKM-IK would incur intensive computational and storage

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42

Sumithra, S., K. R. Remya, and Dr M. N. Giri Prasad. "Automatic Detection and Localization of Macular Edema." Volume 5 - 2020, Issue 9 - September 5, no. 9 (2020): 552–58. http://dx.doi.org/10.38124/ijisrt20sep342.

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Diabetic retinopathy is an eye disease and causes vision loss to the people who are suffering longer from the diabetes. Exudates, bright and red lesions are identified in the diabetic retinal eye. Automatic detection and localization of macular edema is a challenging issue since exudates have non uniform illumination and are low contrasted. Proposed algorithm to detect macular edema encompasses Simple Linear Iterative Clustering, Fisher linear discriminant and Support vector machine classifer. Optic Disc extraction prior to exudates extraction is also introduced. Performance of the proposed de

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43

Huang, Xiang. "Predictive Models: Regression, Decision Trees, and Clustering." Applied and Computational Engineering 79, no. 1 (2024): 124–33. http://dx.doi.org/10.54254/2755-2721/79/20241551.

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This paper explores three fundamental machine learning techniqueslinear regression, k-means clustering, and decision treesand their applications in predictive modeling. In the era of data proliferation, machine learning stands at the intersection of computer science and artificial intelligence, playing a pivotal role in algorithm and model development for enhanced predictions and decision-making. The study delves into the intricacies of these techniques, starting with a focus on linear regression, a supervised learning algorithm for establishing relationships between independent and dependent

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Li, Qiuxia, Tingkui Mu, Hang Gong, et al. "A Superpixel-by-Superpixel Clustering Framework for Hyperspectral Change Detection." Remote Sensing 14, no. 12 (2022): 2838. http://dx.doi.org/10.3390/rs14122838.

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Hyperspectral image change detection (HSI-CD) is an interesting task in the Earth’s remote sensing community. However, current HSI-CD methods are feeble at detecting subtle changes from bitemporal HSIs, because the decision boundary is partially stretched by strong changes so that subtle changes are ignored. In this paper, we propose a superpixel-by-superpixel clustering framework (SSCF), which avoids the confusion of different changes and thus reduces the impact on decision boundaries. Wherein the simple linear iterative clustering (SLIC) is employed to spatially segment the different images

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45

Wang, Shuopeng, Peng Yang, and Hao Sun. "Fingerprinting Acoustic Localization Indoor Based on Cluster Analysis and Iterative Interpolation." Applied Sciences 8, no. 10 (2018): 1862. http://dx.doi.org/10.3390/app8101862.

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Fingerprinting acoustic localization usually requires tremendous time and effort for database construction in sampling phase and reference points (RPs) matching in positioning phase. To improve the efficiency of this acoustic localization process, an iterative interpolation method is proposed to reduce the initial RPs needed for the required positioning accuracy by generating virtual RPs in positioning phase. Meanwhile, a two-stage matching method based on cluster analysis is proposed for computation reduction of RPs matching. Results reported show that, on the premise of ensuring positioning

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Liu, Qingbing. "New Preconditioners for Nonsymmetric Saddle Point Systems with Singular (1,1) Block." ISRN Computational Mathematics 2013 (August 27, 2013): 1–8. http://dx.doi.org/10.1155/2013/507817.

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We investigate the solution of large linear systems of saddle point type with singular (1,1) block by preconditioned iterative methods and consider two parameterized block triangular preconditioners used with Krylov subspace methods which have the attractive property of improved eigenvalue clustering with increased ill-conditioning of the (1,1) block of the saddle point matrix, including the choice of the parameter. Meanwhile, we analyze the spectral characteristics of two preconditioners and give the optimal parameter in practice. Numerical experiments that validate the analysis are presented

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IZONIN, Ivan. "AN UNSUPERVISED-SUPERVISED ENSEMBLE TECHNOLOGY WITH NON-ITERATIVE TRAINING ALGORITHM FOR SMALL BIOMEDICAL DATA ANALYSIS." Computer systems and information technologies, no. 4 (December 28, 2023): 67–74. http://dx.doi.org/10.31891/csit-2023-4-9.

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Improving the accuracy of intelligent data analysis is an important task in various application areas. Existing machine learning methods do not always provide a sufficient level of classification accuracy for their use in practice. That is why, in recent years, hybrid ensemble methods of intellectual data analysis have begun to develop. They are based on the combined use of clustering and classification procedures. This approach provides an increase in the accuracy of the classifier based on machine learning due to the expansion of the space of the input data of the task by the results of the

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Wu, Rouwan, Zhiyong Xu, Jianlin Zhang, and Lihong Zhang. "Robust Global Motion Estimation for Video Stabilization Based on Improved K-Means Clustering and Superpixel." Sensors 21, no. 7 (2021): 2505. http://dx.doi.org/10.3390/s21072505.

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Obtaining accurate global motion is a crucial step for video stabilization. This paper proposes a robust and simple method to implement global motion estimation. We don’t extend the framework of 2D video stabilization but add a “plug and play” module to motion estimation based on feature points. Firstly, simple linear iterative clustering (SLIC) pre-segmentation is used to obtain superpixels of the video frame, clustering is performed according to the superpixel centroid motion vector and cluster center with large value is eliminated. Secondly, in order to obtain accurate global motion estimat

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Wang, Yu, Qi Qi, and Xuanjing Shen. "Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC." Brain Sciences 10, no. 2 (2020): 116. http://dx.doi.org/10.3390/brainsci10020116.

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Non-uniform gray distribution and blurred edges often result in bias during the superpixel segmentation of medical images of magnetic resonance imaging (MRI). To this end, we propose a novel superpixel segmentation algorithm by integrating texture features and improved simple linear iterative clustering (SLIC). First, a 3D histogram reconstruction model is used to reconstruct the input image, which is further enhanced by gamma transformation. Next, the local tri-directional pattern descriptor is used to extract texture features of the image; this is followed by an improved SLIC superpixel segm

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He, Wangpeng, Cheng Li, Yanzong Guo, Zhifei Wei, and Baolong Guo. "A Two-Stage Gradient Ascent-Based Superpixel Framework for Adaptive Segmentation." Applied Sciences 9, no. 12 (2019): 2421. http://dx.doi.org/10.3390/app9122421.

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Superpixel segmentation usually over-segments an image into fragments to extract regional features, thus linking up advanced computer vision tasks. In this work, a novel coarse-to-fine gradient ascent framework is proposed for superpixel-based color image adaptive segmentation. In the first stage, a speeded-up Simple Linear Iterative Clustering (sSLIC) method is adopted to generate uniform superpixels efficiently, which assumes that homogeneous regions preserve high consistence during clustering, consequently, much redundant computation for updating can be avoided. Then a simple criterion is i

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