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Articles by K. Venkatalakshmi
Total Records ( 2 ) for K. Venkatalakshmi
  K. Senthil Kumar , K. Venkatalakshmi and K. Karthikeyan
  Image segmentation is a complex task which helps us to extract information for analysis a digital image. Millions of methods are available for image segmentation. Out of that image thresholding is a simple, efficient and frequently adopted method for image segmentation. Thresholding basically divide a digital image into two regions; foreground and background based on the intensity value of the pixels. The key point in image thresholding is on the optimum value of threshold of the digital image. It is an important and crucial task to select the optimum threshold. A false choice of threshold will lead to poor results in image segmentation. Generally optimization algorithms are used to select the optimum threshold value. Artificial Bee Colony (ABC) algorithm is one of the optimization algorithms which are the replica of natural behaviour of honey bees to find abundant nectar amount. This study describes an approach to segment an 8 bit human lung image using artificial bee colony algorithm based thresholding method. The proposed method proves that the uniformity factor in the image segmentation is good relative to other conventional methods.
  K. Venkatalakshmi , P. Anisha Praisy , R. Maragathavalli and S. Mercy Shalinie
  An attempt has been made in this study to find globally optimal cluster centers for multispectral images with Enhanced Genetic k-Means algorithm. The idea is to avoid the expensive crossover or fitness to produce valid clusters in pure GA and to improve the convergence time. The drawback of using pure GA in this problem is the usage of an expensive crossover or fitness to produce valid clusters (Non-empty clusters). To circumvent the disadvantage of GA, hybridization of GA with k-Means as Genetic k-Means is already proposed. This Genetic k-Means Algorithm (GKA) always finds the globally optimal cluster centers but the drawback is the usage of an expensive fitness function which involves σ truncation. The Enhanced GKA alleviates the problem by using a simple fitness function with an incremental factor. A k-Means operator (one-step of k-Means algorithm) used in GKA as a search operator is adopted in this study. In Enhanced GKA the mutation involves less computation than the mutation involved in GKA. In order to avoid the invalid clusters formed during the iterations the empty clusters are converted into singleton cluster by adding a randomly selected data item until none of the cluster is empty. The results show that the proposed algorithm converges to the global optimum in fewer numbers of generations than conventional GA and also found to consume less computational complexity than GKA. It proves to be an effective clustering algorithm for multispectral images.
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