Image Transformation using Modified K- means clustering algorithm for Parallel saliency map
Aman Sharma, Student Cse, Bala Krushna Tripathy, Krishnan Nallaperumal · 2013
to design an image transformation system is Depending on the transform chosen, the input and output images may appear entirely different and have different interpretations. Image Transformation with the help of certain module like input image, image cluster index, object in cluster and color index transformation of image. K-means clustering algorithm is used to cluster the image for better segmentation. In the proposed method parallel saliency algorithm with K-means clustering is used to avoid local minima and to find the saliency map. The region behind that of using parallel saliency algorithm is proved to be more than exiting saliency algorithm. Keyword- parallel saliency algorithm, Image Transformation, saliency map, K- means clustering algorithm, morphology. I. INTRODUCTION Parallel saliency algorithm is much better than exiting saliency algorithm in terms of performance. Parallel saliency algorithm implemented with the help of image signature as well as channel map for producing a saliency map. Image signature, within the region of signal mixing helps in approximating the foreground of an image. Then it is studied through various experiments whether this approximate foreground overlaps with locations, which are visually conspicuous. Parallel saliency algorithm playing the major role for image processing researches for developing new algorithms using saliency map, improving exiting algorithms using saliency mapping concept in parallel environment approach. In this paper Image transformation with clustering concepts and the results are retrieved from one by one image parallel saliency algorithm, which is finally produces a saliency map.