Indian Journal of Science and Technology

Abstract

Background/Objectives: To interactively split an object of interest from the remaining image with better smoothing by introducing improved adaptive Markov Random field resolving the problem of much noise. Methods/Statistical analysis: By utilizing a Dirichlet process multiple - view learning scheme, the unlabelled pixel labels are calculated by using the seed pixels. It is used for supporting the multiple-view learning in order to incorporate the constraints of both appearance and boundary constraint, and the Dirichlet process mixture-based nonlinear classification for concurrently modelling the image features and distinguishing the differences between the classes of object and background. The MRF field is utilized for providing the smoothness in segment labels. Findings: In Markov Random Field (MRF) based scheme, only the pixel and the surrounding pixels relationships are considered. The microscopic image processing result along with low noise is bad. To solve this problem, the adaptive MRF method based on region; it exploits Graph Cuts for inference. The different type of images produces the different pre-segmentation results. The connection degree between current and its linked regional blocks is denoted by connection parameter. If the connection parameter value is high, large connection degree between regional block and neighbouring regional blocks is defined. Otherwise lower connection degree between regional block and neighbourhood regional blocks. The adaptive MRF smoothing is more accurate and efficient segmentation result than the traditional MRF based Smoothing method. However, the appropriate parameter selection is a difficult task for practical image segmentation which can be solved by introducing an improved adaptive MRF model by using a modified graph cutter. Thus, the image segmentation is achieved based on modified graph-cut model using a novel energy function without the regularizing parameter. Improvements/Applications: The improved adaptive Markov Random Field approach interactively segment images with better smoothness than most of the current approaches.

Keywords: Boundary Constraints, Interactive Segmentation, Markov Random Field