Unsupervised Learning for Data Clustering Based Image Segmentation

2019 ◽  
pp. 63-84
Author(s):  
Xiaochun Wang ◽  
Xiali Wang ◽  
Don Mitchell Wilkes
Keyword(s):  
Image Segmentation ◽  
Unsupervised Learning ◽  
Data Clustering

Clustering Techniques and Their Applications: A Review

2020 ◽  
Vol 1 (4) ◽  
pp. 1-6
Author(s):  
Arjun Dutta
Keyword(s):  
Data Mining ◽  
Image Segmentation ◽  
Unsupervised Learning ◽  
Clustering Analysis ◽  
Data Clustering ◽  
Clustering Method ◽  
Clustering Techniques ◽  
Partitional Clustering ◽  
Gene Information ◽  
Scientific Fields

This paper deals with concise study on clustering: existing methods and developments made at various times. Clustering is defined as an unsupervised learning where the targets are sorted out on the foundation of some similarity inherent among them. In the recent times, we dispense with large masses of data including images, video, social text, DNA, gene information, etc. Data clustering analysis has come out as an efficient technique to accurately achieve the task of categorizing information into sensible groups. Clustering has a deep association with researches in several scientific fields. k-means algorithm was suggested in 1957. K-mean is the most popular partitional clustering method till date. In many commercial and non-commercial fields, clustering techniques are used. The applications of clustering in some areas like image segmentation, object and role recognition and data mining are highlighted. In this paper, we have presented a brief description of the surviving types of clustering approaches followed by a survey of the areas.

Unsupervised Learning of Affinity for Image Segmentation: An Inpainting based Approach

2021 ◽  
Author(s):  
Swati Bhugra ◽  
Vinay Kaushik ◽  
Isaac Castro Mateos ◽  
Santanu Chaudhury ◽  
Brejesh Lall
Keyword(s):  
Image Segmentation ◽  
Unsupervised Learning

PENERAPAN DATA MINING DALAM MENGELOMPOKKAN KUNJUNGAN WISATAWAN DI KOTA YOGYAKARTA MENGGUNAKAN METODE K-MEANS

2021 ◽  
Vol 1 (1) ◽  
pp. 27-32
Author(s):  
Bambang Setio ◽  
Putri Prasetyaningrum
Keyword(s):  
Data Mining ◽  
Unsupervised Learning ◽  
Data Clustering ◽  
Cluster 2

Yogyakarta merupakan salah satu kota di Indonesia yang memiliki daya tarik wisata dan merupakan kota tujuan wisata yang paling diminati oleh wisatawan, dilihat dari jumlah kunjungan wisatawan yang semakin naik dari tahun ke tahun. Selain sebagai kota wisata, Yogyakarta merupakan kota pelajar, kota budaya dan kota perjuangan. Karena Yogyakarta disebut sebagai kota wisata, banyak berbagai macam objek wisata yang ditawarkan oleh Kota Yogyakarta. Dalam hal ini, penerapan datamining mampu menjadi solusi dalam menganalisa data. Clustering termasuk ke dalam descriptive methods, dan juga termasuk unsupervised learning dimana tidak ada pendefinisian kelas objek sebelumnya. Sehingga clustering dapat digunakan untuk menentukan label kelas bagi data-data yang belum diketahui kelasnya. Metode K-Means termasuk dalam partitioning clustering yang memisahkan data ke daerah bagian yang terpisah. Metode K-Means sangat terkenal karena kemudahan dan kemampuannya untuk mengelompokkan data besar dan outlier dengan sangat cepat. dari data yang diinputkandan telah di proses melalui metode algoritma K-Means bahwa telah melakukan iterasi sebanyak 5 kali dengan memilih cluster 1, cluster 2, cluster 3 secara acak (random) dengan cluster 1 memiliki 24 data dengan persentase sebesar (50%), cluster 2 memiliki 11 data dengan persentase sebesar (23%),  dan cluster 3 memiliki 13 data dengan persentase sebesar (27%).  

Categorization of Data Clustering Techniques

2008 ◽  
pp. 568-577
Author(s):  
Baoying Wang ◽  
Imad Rahal ◽  
Richard Leipold
Keyword(s):  
Unsupervised Learning ◽  
Supervised Learning ◽  
Data Clustering ◽  
Analysis Data ◽  
Discovery Process ◽  
Data Set ◽  
Market Basket ◽  
Clustering Techniques ◽  
Data Points ◽  
Class Labels

Data clustering is a discovery process that partitions a data set into groups (clusters) such that data points within the same group have high similarity while being very dissimilar to points in other groups (Han & Kamber, 2001). The ultimate goal of data clustering is to discover natural groupings in a set of patterns, points, or objects without prior knowledge of any class labels. In fact, in the machine-learning literature, data clustering is typically regarded as a form of unsupervised learning as opposed to supervised learning. In unsupervised learning or clustering, there is no training function as in supervised learning. There are many applications for data clustering including, but not limited to, pattern recognition, data analysis, data compression, image processing, understanding genomic data, and market-basket research.

VHR satellite image segmentation based on topological unsupervised learning

2015 ◽  
Author(s):  
Nistor Grozavu ◽  
Nicoleta Rogovschi ◽  
Guenael Cabanes ◽  
Andres Troya-Galvis ◽  
Pierre Gancarski
Keyword(s):  
Image Segmentation ◽  
Unsupervised Learning ◽  
Satellite Image

scholarly journals Remote Sensing by using Unsupervised Algorithm

2021 ◽  
Author(s):  
Afshan Saleem
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Unsupervised Learning ◽  
Hyperspectral Image ◽  
Relevant Information ◽  
Spectral Signature ◽  
Sensing Applications ◽  
Hyper Spectral ◽  
Wide Range ◽  
Remote Sensing Applications

Hyper-spectral images contain a wide range of bands or wavelength due to which they are rich in information. These images are taken by specialized sensors and then investigated through various supervised or unsupervised learning algorithms. Data that is acquired by hyperspectral image contain plenty of information hence it can be used in applications where materials can be analyzed keenly, even the smallest difference can be detected on the basis of spectral signature i.e. remote sensing applications. In order to retrieve information about the concerned area, the image has to be grouped in different segments and can be analyzed conveniently. In this way, only concerned portions of the image can be studied that have relevant information and the rest that do not have any information can be discarded. Image segmentation can be done to assort all pixels in groups. Many methods can be used for this purpose but in this paper, we discussed k means clustering to assort data in AVIRIS cuprite, AVIRIS Muffet and Rosis Pavia in order to calculate the number of regions in each image and retrieved information of 1st, 10th and100th band. Clustering has been done easily and efficiently as k means algorithm is the easiest approach to retrieve information.

Fast and Robust Fuzzy C-Means Algorithms for Automated Brain MR Image Segmentation

2008 ◽  
pp. 578-586
Author(s):  
László Szilágyi ◽  
Sándor Miklós Szilágyi ◽  
Zoltán Benyó
Keyword(s):  
Image Segmentation ◽  
Data Clustering ◽  
Noise Sensitivity ◽  
Gray Level ◽  
Strongly Correlated ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Relevant Property ◽  
Imaging Artifacts ◽  
Mr Image Segmentation

By definition, image segmentation represents the partitioning of an image into nonoverlapping, consistent regions, which appear to be homogeneous with respect to some criteria concerning gray level intensity and/or texture. The fuzzy c-means (FCM) algorithm is one of the most widely used method for data clustering, and probably also for brain image segmentation (Bezdek & Pal., 1991). However, in this latter case, standard FCM is not efficient by itself, as it is unable to deal with that relevant property of images that neighbor pixels are strongly correlated. Ignoring this specificity leads to strong noise sensitivity and several other imaging artifacts.

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