A comparison of several vector quantization codebook generation approaches

Huang, Chien-Min; Harris, Richard W.

doi:10.1109/83.210871

Cited by 81 publications

(38 citation statements)

References 17 publications

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“…Huang and Harris [22], the Direct Search Binary splitting (DSBS) technique was proposed. This technique is comparable to the Binary splitting method higher than except that the dividing step is increased through the utilization of Principle component Analysis (PCA).…”

Section: Standard K-means Clustering Algorithmmentioning

confidence: 99%

Performance Analysis of Improved K-Means & K-Means in Cluster Generation

Shanthi¹,

.M²

2014

IJAREEIE

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K-means is the well known and most familiar algorithm among the other partition based clustering algorithms. It typically shows spectacular results even in significantly massive information sets of Image segmentation supported associate adaptive K-means clustering algorithm is conferred. The proposed method tries to develop Kmeans algorithm to get high performance and potency. This technique proposes data formatting step in K-means algorithmic rule. additionally, it solves a model choice variety by deciding the quantity of clusters victimization datasets from image by frame size and also the definite quantity between the means that, and extra steps for convergence step in K-means algorithm are supplementary. Moreover, so as to judge the performance of the proposed technique, the results of the proposed technique, customary K-means and recently changed K-means are compared. The experimental results showed that the proposed technique provides higher output.

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Section: Standard K-means Clustering Algorithmmentioning

confidence: 99%

Performance Analysis of Improved K-Means & K-Means in Cluster Generation

Shanthi¹,

.M²

2014

IJAREEIE

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“…Many codebook generation and quantization algorithms were developed and tested for compression [27,28,29,30]. Nowadays it has shown its value in other applications as well, such as pattern recognition and face detection [31,32], image segmentation [33], tumor demarcation in MRI and Mammogram images [34,35], content based image retrieval [36] etc.…”

Section: Vector Quantizationmentioning

confidence: 99%

Tumour Delineation using Statistical Properties of The Breast US Images and Vector Quantization based Clustering Algorithms

Kekre¹,

Shrinath²

2013

IJIGSP

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-Breast cancer is most common and leading cause of death among women. With improvement in the imaging modalities it is possible to diagnose the cancer at an early stage moreover treatment at an early stage reduces the mortality rate. B-mode ultrasound (US) imaging is very illustrious and reliable technique in early detection of masses in the breast. Though it is complimentary to the mammography, dense breast tissues can be examined more efficiently and detects the small nodules that are usually not observed in mammography. Segmentation of US images gives the clear understanding of nature and growth of the tumor. But some inherent artifact of US images makes this process difficult and computationally inefficient. Many methods are discussed in the literature for US image segmentation, each method has its pros and cons. In this paper, initially region merging based watershed and marker-controlled watershed transforms are discussed and implemented. In the subsequent sections we proposed a method for segmentation, based on clustering. Proposed method consists of three stages, in first stage probability images and its equalized histogram images are obtained from the original US images without any preprocessing. In the next stage, we used VQ based clustering technique with LBG, KPE and KEVR codebook generation algorithm followed by sequential cluster merging. Last stage is the post processing, where we removed unwanted regions from the selected cluster image by labeling the connected components and moreover used morphological operation for closing the holes in the final segmented image. Finally, results by our method are compared with initially discussed methods.

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“…This is not robust for large data bases. Huang and Harris [36] projected a method called direct search binary splitting (DSBS) based on the principal component analysis (PCA) and the vector of Linde et al [33]. Thiesson et al [37] designed an algorithm that depends on the mean value of the given data.…”

Section: Literature Surveymentioning

confidence: 99%

HK-Means: A Heuristic Approach to Initialize and Estimate the Number of Clusters in Biological Data

2016

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K-means algorithm is one of the simplest and fastest clustering algorithms existing since more than four decades. One of the limitations of this algorithm is estimating number of clusters in advance. This algorithm also suffers from random initialization problem. This paper proposes a heuristic which initializes the cluster centers and estimates the number of clusters as a discrete value. The method estimates the number of clusters and initializes many cluster centers successfully for the clusters that are dense and separated significantly. The method selects a new cluster center in each iteration. The point selected is the point which is most dissimilar from the previously chosen points. The proposed algorithm is experimented on various synthetic data and the results are encouraging.

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A comparison of several vector quantization codebook generation approaches

Cited by 81 publications

References 17 publications

Performance Analysis of Improved K-Means & K-Means in Cluster Generation

Performance Analysis of Improved K-Means & K-Means in Cluster Generation

Tumour Delineation using Statistical Properties of The Breast US Images and Vector Quantization based Clustering Algorithms

HK-Means: A Heuristic Approach to Initialize and Estimate the Number of Clusters in Biological Data

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