Remote sensing image segmentation algorithm based on AP clustering

Clustering analysis is an important technology in data mining, and it has been widely used in areas such as statistics, image processing, medical diagnosis, information retrieval, biology and machine learning. Image segmentation plays a fundamental role in computer vision as a requisite step in such tasks as object detection, classification, and tracking. The average of the color vectors in each region is calculated and considered as an input data point of AP algorithm. Distances between data points are regards as similarity measure index, and then the AP algorithm is applied to perform globally optimized clustering and segmentation based on similarity matrix. At last, we give a suit of segmentation evaluation system. The method uses fuzzy degree to measure segmentation quality. A new non-linear mapping function of changing from special field to fuzzy property field is proposed, which differs from the one used in image segmentation. Construct the model of evaluation and evaluate segmentation results of these segmentation methods by using many real images. © 2014 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.