Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center of Intelligent Perception and Computation, International Collaboration Joint Lab in Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi’an, China
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center of Intelligent Perception and Computation, International Collaboration Joint Lab in Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi’an, China
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center of Intelligent Perception and Computation, International Collaboration Joint Lab in Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi’an, China
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center of Intelligent Perception and Computation, International Collaboration Joint Lab in Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi’an, China
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center of Intelligent Perception and Computation, International Collaboration Joint Lab in Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi’an, China
This article presents a two-stage evolutionary fuzzy clustering framework for noisy image segmentation. It is a bi-stage system comprising a multi-objective optimization stage and a fuzzy clustering segmentation stage. In the multi-objective optimization stage, the fuzzy clustering on pixels in inhomogeneous regions is converted into a multi-objective problem, which can preserve image details while restraining noise. The multi-objective problem is decomposed into several sub-problems by the Tchebycheff approach. A trade-off can be obtained by optimizing these sub-problems simultaneously. In the fuzzy clustering segmentation stage, fuzzy clustering with the trade-off between preserving image details and restraining noise is performed on the whole observed image. To deal with this fuzzy clustering problem, an adaptive evolutionary fuzzy clustering algorithm with spatial information is proposed. Experiment results on synthetic and real images illustrate the effectiveness of the proposed framework for noisy image segmentation.
