The orientation and focus of this research piece are the extraction of the foreground and the compositing of this extracted region into another background. This phenomenon is termed as, 'Image Matting', which is frequently employed in film production or the digital media world. The proposed method approaches the ill-posed nature of image matting via a non-parametric sampling-based method along with the clustering technique known as 'Superpixel'. In the proposed method, pixels of the entire image(s) tend to gather in close proximity under one unit (Superpixel) with respect to color, intensity, and texture. This gathering in close proximity reduces the search space more than 20 times and helps in efficiently finding the association of unknown regions with the samples from the background and foreground. The use of samples facilitates the pixel color assimilating with local image structure, which is significant to calculate a good resultant alpha matte particularly in the image having complex texture, and in natural images. As per my knowledge and study, the matting problem using centroids of Superpixels has not previously been explored. Results are evaluated on different images on an online standard open-source dataset, available for image matting. Results are comparable to the different matting algorithms applied independently on images of the dataset. Confidence and ameliorated refinements of proposed methods are evident in the obtained results compared to other image matting methods.