A Review on Image Segmentation with its Clustering Techniques

Sharma, Priyansh; Suji, Jenkin

doi:10.14257/ijsip.2016.9.5.18

Cited by 34 publications

(17 citation statements)

References 13 publications

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“…5. In [7] this paper provides survey of several approaches of segmentation methods with their clustering techniques.…”

Section: Related Workmentioning

confidence: 99%

Comparing the Main Approaches of Image Segmentation

Amir Abdullah Karim,

Ali Sameer

2021

Iraqi Journal of Science

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Images are important medium for conveying information; this makes improvement of image processing techniques also important. Interpretation of image content is one of the objectives of image processing techniques. Image interpretation that segments the image to number of objects called image segmentation. Image segmentation is an important field to deal with the contents of images and get non overlapping regions coherent in texture and color, it is important to deal only with objects with significant information. This paper presents survey of the most commonly used approaches of image segmentation and the results of those approaches have been compared and according to the measurement of quality presented in this paper the Otsu's threshold method give the best result with less time.Keywords: segmentation, threshold, region growing, region split and merge. ‫االساسية‬ ‫الطرق‬ ‫بين‬ ‫مقارنة‬ ‫الصورة‬ ‫لتجزئة‬ ‫كريم‬ ‫اهلل‬ ‫عبد‬ ‫االمير‬ ‫عبد‬ ، ‫سمير‬ ‫عمي‬ ‫رفل‬ * ‫الخالصة‬

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“…5. In [7] this paper provides survey of several approaches of segmentation methods with their clustering techniques.…”

Section: Related Workmentioning

confidence: 99%

Comparing the Main Approaches of Image Segmentation

Amir Abdullah Karim,

Ali Sameer

2021

Iraqi Journal of Science

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“…The final method is to directly apply clustering without using any temporal cut/window hypotheses and in steal consider the collected multivariate points. Many clustering methods can be applied and they are often used for image segmentation problems [7]. The direct K-means (KM) and hierarchical clustering (HC) methods are the most common and are used for many applications.…”

Section: The State-of-the-art From An Algorithmic Point Of Viewmentioning

confidence: 99%

Comparative Study of Clustering Approaches Applied to Spatial or Temporal Pattern Discovery

Grassi

Caillault

Bigand

et al. 2020

JMSE

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In the framework of ecological or environmental assessments and management, detection, characterization and forecasting of the dynamics of environmental states are of paramount importance. These states should reflect general patterns of change, recurrent or occasional events, long-lasting or short or extreme events which contribute to explain the structure and the function of the ecosystem. To identify such states, many scientific consortiums promote the implementation of Integrated Observing Systems which generate increasing amount of complex multivariate/multisource/multiscale datasets. Extracting the most relevant ecological information from such complex datasets requires the implementation of Machine Learning-based processing tools. In this context, we proposed a divisive spectral clustering architecture—the Multi-level Spectral Clustering (M-SC) which is, in this paper, extended with a no-cut criteria. This method is developed to perform detection events for data with a complex shape and high local connexity. While the M-SC method was firstly developed and implemented for a given specific case study, we proposed here to compare our new M-SC method with several existing direct and hierarchical clustering approaches. The clustering performance is assessed from different datasets with hard shapes to segment. Spectral methods are most efficient discovering all spatial patterns. For the segmentation of time series, hierarchical methods better isolated event patterns. The new M-SC algorithm, which combines hierarchical and spectral approaches, give promise results in the segmentation of both spatial UCI databases and marine time series compared to other approaches. The ability of our M-SC method to deal with many kinds of datasets allows a large comparability of results if applies within a broad Integrated Observing Systems. Beyond scientific knowledge improvements, this comparability is crucial for decision-making about environmental management.

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“…In a cluster the query method employ is Euclidean distance [7] based on the clustering pattern. The clustering pattern can be divided into 5 methods [8,9] which are:…”

Section: The Clustering Techniquesmentioning

confidence: 99%

Pixel Value Graphical Password Scheme: Compatibility of K-means Clustering Algorithm as Pixel Value Password Fault Tolerance Mechanism

Shukran¹,

Yunus²,

Abdullah³

et al. 2019

IJSMIT

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In September 2018, the patent for pixel value graphical password scheme was granted in Malaysia. The graphical password scheme was designed to reduce the complexity of previously developed graphical password scheme where a user only requires to load their personal image as password instead of complex graphical challenge during authentication. As the guardian of digital access, Pixel Value Access Control was highly invincible from password pixel forgery attack where a little bit different pixel value derived from loaded image will deny the access. Only the original enrolled image from a registered user can be recognized by Pixel Value Access Control to authenticate the respective username. That fact makes the graphical password scheme is a trusted access control mechanism but, on the other hand, it makes users bound with the only original password pixel image file. Thus, Pixel Value Access Control need to be installed the pixel value fault tolerance mechanism where it could allow users to acquire their password pixel image file from various storage media. The clustering technique was suggested to solve this issue where it allows an altered pixel password being recognized as the same group of the original pixel password. There are number of clustering algorithms developed for various purposed and application of digital image clustering. K-Means algorithm is one the partition-based clustering algorithm that found to be the simplest and fastest clustering algorithm as suggested by many researchers. This paper is mainly to exhibit the selection of K-Means clustering algorithm became the crucial algorithm for Pixel Value Access Control password pixel fault tolerance algorithm. Background of this topic was briefly explained in introduction section, the implementation of K-Means algorithm as Pixel Value Access Control fault tolerance was elaborate in section 2 and followed by validation of the implementation in section 3. At the end of this paper, there is conclusion for this study and followed by list of references.

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A Review on Image Segmentation with its Clustering Techniques

Abstract: Segmentation refers to a technique in which an image in digital form

Cited by 34 publications

References 13 publications

Comparing the Main Approaches of Image Segmentation

Comparing the Main Approaches of Image Segmentation

Comparative Study of Clustering Approaches Applied to Spatial or Temporal Pattern Discovery

Pixel Value Graphical Password Scheme: Compatibility of K-means Clustering Algorithm as Pixel Value Password Fault Tolerance Mechanism

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