This paper is mainly aimed at the decomposition of image quality assessment study by using Three Parameter Logistic Mixture Model and k-means clustering (TPLMM-k). This method is mainly used for the analysis of various images which were related to several real time applications and for medical disease detection and diagnosis with the help of the digital images which were generated by digital microscopic camera. Several algorithms and distribution models had been developed and proposed for the segmentation of the images. Among several methods developed and proposed, the Gaussian Mixture Model (GMM) was one of the highly used models. One can say that almost the GMM was playing the key role in most of the image segmentation research works so far noticed in the literature. The main drawback with the distribution model was that this GMM model will be best fitted with a kind of data in the dataset. To overcome this problem, the TPLMM-k algorithm is proposed. The image decomposition process used in the proposed algorithm had been analyzed and its performance was analyzed with the help of various performance metrics like the Variance of Information (VOI), Global Consistency Error (GCE) and Probabilistic Rand Index (PRI). According to the results, it is shown that the proposed algorithm achieves the better performance when compared with the previous results of the previous techniques. In addition, the decomposition of the images had been improved in the proposed algorithm.
In machine learning, clustering is recognized as widely used task to find hidden structure of data. While handling the massive amount of data, the traditional clustering algorithm degrades in performance due to size and mixed type of attributes. The Removal Dependency on K and Initial Centroid Selection (REDIC) algorithm is designed to handle mixed data with frequency based dissimilarity measurement for categorical attributes. The selection of initial centroids and prior decision for number of cluster improves the efficiency of REDIC algorithm. To deal with the large scale data, the REDIC algorithm is migrated to Map Reduce paradigm,and Map Reduce based REDIC(MR-REDIC) algorithm is proposed. The large amount of data is divided into small chunks and parallel approach is used to reduce the execution time of algorithm.The proposed algorithm inherits the feature of REDIC algorithm to cluster the data.The algorithm is implemented in Hadoop environment with three different configuration and evaluated using five bench mark data sets. Experimental results show that the Speed up value of data is gradually shifting towards linear by increasing number of data nodes from one to four. The algorithm also achieves the near to closer value for Scale up parameter, while maintaining the accuracy of algorithm.
For image analysis image decomposition or segmenting the images is a basic requirement. For decomposing the images probability models play a vital role. This paper addresses image decomposition using three parameter logistic type mixture distribution. Here it is assumed that the pixel intensities of image region follow a three parameter logistic type probability distribution. The estimation of parameters is carried utilizing Expectation and Maximization algorithm. The initialization of the parameters is done with K-means algorithm and moment method of estimation the number of image regions is obtained counting the peaks of the histogram drawn for the pixel intensities of the whole image. The decomposition algorithm (segmentation) is developed under maximum component likelihood function with Bayesian considerations. The efficiency of the proposed algorithm is studied by computing the metrics for segmentation such as GCE, VOI, PRI. The experimentation is conducted with five randomly chosen images taken from Berkeley image database revealed that the proposed algorithm is superior to the other model based segmentation algorithms for some images, which are having laptykurtic image regions.A comparative study with that of segmentation algorithm based on GMM is also presented.
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