Iterative Refinement of Possibility Distributions by Learning for Pixel-Based Classification

Abstract

This paper proposes an approach referred as: iterative refinement of possibility distributions by learning (IRPDL) for pixel-based image classification. The IRPDL approach is based on the use of possibilistic reasoning concepts exploiting expert knowledge sources as well as ground possibilistic seeds learning. The set of seeds is constructed by incrementally updating and refining the possibility distributions. Synthetic images as well as real images from the RIDER Breast MRI database are being used to evaluate the IRPDL performance. Its performance is compared with three relevant reference methods: region growing, semi-supervised fuzzy pattern matching, and Markov random fields. The IRDPL performance (in terms of recognition rate, 87.3%) is close to the Markovian method (88.8%) that is considered to be the reference in pixel-based image classification. IRPDL outperforms the other two methods, respectively, at the recognition rates of 83.9% and 84.7%. In addition, the proposed IRPDL requires fewer parameters for the mathematical representation and presents a reduced computational complexity.

An intelligent quality-based approach to fusing multi-source possibilistic information

“…Table 16 for the impoverished datasets, show the advantage of the possibility theory in data modeling compared to the probability theory and the SVM classifier in poor data environments (information incompleteness). Same kind of advantages have been confirmed from results that have been obtained for different kind of applications namely in pattern recognition and image segmentation in the processing of poor-quality images [32,[34][35][36][37].…”

Classification of magnetic resonance images for brain tumour detection

“…A region grows iteratively by merging neighbourhood pixels to the similar initial seed class regions. For region growing, possibilistic knowledge diffusion (PKD) method is used [22]. In this method, region growing is done with the possibilistic map that is possibilistic knowledge representation instead of grey level.…”

“…In the third step, non‐linear normed residual masking is employed on the outcome of the second step for region extraction [21]. In the final step, the possibilistic model is used for region refinement [22]. The refinement process is used to confer sharp edges, outlier, and consistent boundaries to an abnormal region.…”

Probability-Possibility Transformation under Perspective of Random-fuzzy Dual Interpretation of Unknown Uncertainty

“…As an alternative method to fuzzy sets for handling fuzzy uncertainty, possibility theory has in recent decades received extensive attention [1][2][3][4][5][6][7][8][9][10][11][12][44][45][46]. It was lately demonstrated that probability and possibility are customized for the measure of randomness and fuzziness, respectively [6].…”