Improved quantum-inspired immune clonal clustering algorithm applied to SAR image segmentation

Author

Li, Y.Y. ; Wu, N.N. ; Liu, R.C.

Author_Institution

Key Lab. of Intell. Perception & Image Understanding of Minist. of Educ. of China, Xidian Univ., Xi´´an, China

Volume

2

fYear

2011

fDate

24-27 Oct. 2011

Firstpage

1566

Lastpage

1569

Abstract

The goal of segmentation is to partition an image into disjoint regions. In this paper, the segmentation problem based on partition clustering is viewed as a combinatorial optimization problem. The original image is partitioned into small blocks by watershed algorithm, and the quantum-inspired immune clonal algorithm is used to search the optimal clustering centre, and make the sequence of maximum affinity function as clustering result, and finally obtain the segmentation result. Experimental results show that the proposed method is effective for SAR image segmentation.

Keywords

combinatorial mathematics; image classification; image segmentation; image sequences; optimisation; pattern clustering; radar imaging; synthetic aperture radar; SAR; affinity function; combinatorial optimization problem; image partition clustering; image segmentation; image sequence; quantum-inspired immune clonal algorithm; watershed algorithm; Clustering algorithms; Evolutionary computation; Feature extraction; Image segmentation; Indexes; Partitioning algorithms; Radiative recombination; SAR; Segmentation problem; partition clustering; quantum-inspired immune clonal algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Radar (Radar), 2011 IEEE CIE International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4244-8444-7

Type

conf

DOI

10.1109/CIE-Radar.2011.6159862

Filename

6159862