An efficient clustering scheme using support vector methods

Nath, J. Saketha; Shevade, Shirish

doi:10.1016/j.patcog.2006.03.012

Cited by 14 publications

(4 citation statements)

References 6 publications

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“…SVM is a class of supervised machine-learning algorithm which can be used for both classification or regression problems [54], [55]. SVM operates on an n-dimensional feature space with the value of each feature being the value of a particular coordinate.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Heterogeneity-Aware Local Binary Patterns for Retrieval of Histopathology Images

et al. 2019

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Histopathology images exhibit considerable variability, which can make diagnosis prone to uncertainty and errors. Using retrieval systems to locate similar images when a query image is given can assist pathologists in making more reliable decisions when diagnosing, based on accurately diagnosed past cases. Local binary patterns (LBP) have been successfully used as image descriptors for different applications. However, using LBP on histopathology images is still under investigation from different perspectives. The immense texture variability of these images and the lack of labeled training data are among the challenges that must be addressed. In this paper, we propose a new extension of LBP that is explicitly aware of the heterogeneity of local texture patterns through heterogeneity-based weighting. We use both homogeneity and the second moment (variance) of local neighborhoods to extend LBP histograms with heterogeneity information so that they better capture the polymorphism in histopathology images. We applied all concepts at multiple scales and investigated the extensions through both separated and concatenated extended histograms. We applied the proposed method on three publicly available datasets, namely, KimiaPath24, invasive ductal carcinoma (IDC), and BreakHis. The experimental results revealed that our features could retrieve and classify images with the highest accuracy. For KimiaPath24, we achieved 96.4% surpassing both LBP (91.0%) and deep features from VGG network (79.5%). For IDC, we reached the highest F1 measure (0.7665). Only for BreakHis, the proposed method was slightly less accurate than a customized convolutional neural network with rule fusion.INDEX TERMS Content based image retrieval (CBIR), whole slide image (WSI) processing, heterogeneity mask, local binary pattern (LBP).

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Heterogeneity-Aware Local Binary Patterns for Retrieval of Histopathology Images

et al. 2019

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“…The second phase was then to label the candidate points. Nath and Shevade [4] presented a novel approach that increases the efficiency of the SVC scheme. The geometry presented in the clustering problem was exploited to reduce the training data size.…”

Section: Related Workmentioning

confidence: 99%

Improvement of Pre-processing Capacity of Support Vector Clustering using Neural Network Kernel Function for Stream Data Classification

Chatterjee¹,

Shrivastav²,

Richhariya³

2014

IJCA

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Pre-processing of data before generation of pattern or classification is major steps. In the phase of pre-processing reduces the noise level of data using different technique of data mining. In current research trend support vector clustering is used for efficient data processing for noise reduction and pattern generation. Support vector clustering is new paradigm of data mining tools. It combined with supervised learning and unsupervised learning. for the success story behind support vector clustering technique is kernel function. The better selection of kernel function produces better result in terms of noise reduction and classification. In this paper proposed an improved support vector clustering method using neural network kernel function for stream data classification. The neural network function work as data optimizer and data selector in support vector clustering.

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“…The algorithm only needs to label the selected stable equilibrium points at IJICC 1,4 which all the other points converge. Nath and Shevade (2006) proposed an additional pre-processing step to remove the data points that are not important to the clustering, i.e. non-SVs.…”

Section: Recent Advances In Cluster Analysismentioning

confidence: 99%

Recent advances in cluster analysis

Wunsch

2008

International Journal of Intelligent Computing and Cybernetics

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Purpose -The purpose of this paper is to provide a review of the issues related to cluster analysis, one of the most important and primitive activities of human beings, and of the advances made in recent years. Design/methodology/approach -The paper investigates the clustering algorithms rooted in machine learning, computer science, statistics, and computational intelligence. Findings -The paper reviews the basic issues of cluster analysis and discusses the recent advances of clustering algorithms in scalability, robustness, visualization, irregular cluster shape detection, and so on. Originality/value -The paper presents a comprehensive and systematic survey of cluster analysis and emphasizes its recent efforts in order to meet the challenges caused by the glut of complicated data from a wide variety of communities.

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An efficient clustering scheme using support vector methods

Cited by 14 publications

References 6 publications

Heterogeneity-Aware Local Binary Patterns for Retrieval of Histopathology Images

Heterogeneity-Aware Local Binary Patterns for Retrieval of Histopathology Images

Improvement of Pre-processing Capacity of Support Vector Clustering using Neural Network Kernel Function for Stream Data Classification

Recent advances in cluster analysis

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