Image segmentation based on data field and cloud model

Abstract

There are many uncertainties in image segmentation, which needs theories and methods with uncertainty to handle. This paper proposes a novel method of image segmentation based on data field and cloud model, which considers the spatial information of image through data field, and handles the uncertainty of image through cloud model. The proposed method inspired from cognitive physics considers each pixel as a physical object, calculates the interactive force of these physical objects, and generates image data field and the potential values which are considered as spatial information. And then, uses cloud transformation and magnitude cloud synthesis to extract the concepts of potential-frequency histogram from low level to high level, realizes the clustering of pixels, finally uses maximum determination to partition the pixels into different classes and segment image into different regions. Results of many experiments indicate that the proposed method obtains better effect than those of Fuzzy C-means clustering, Otsu and cloud based hierarchical method, and it is feasible and effective.