EMBoost clustering based on spatial information for image segmentation

Shuiping Gou; Quanhua Fei; Yifan Zhao

doi:10.1117/12.902084

8 December 2011 EMBoost clustering based on spatial information for image segmentation

Shuiping Gou, Quanhua Fei, Yifan Zhao

Proceedings Volume 8003, MIPPR 2011: Automatic Target Recognition and Image Analysis; 800315 (2011) https://doi.org/10.1117/12.902084
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

Abstract

Compared with the traditional EM clustering algorithm, the EMBoost clustering algorithm can improve two problems that the sensitive result to initial value and the low precision. However, an important factor, the local information, is not considered in the EMBoost algorithm, which is useful to enhance the performance of the EMBoost algorithm, especially for image segmentation. We believe that neighbor pixels to the center measured by the space distance and the texture distance are beneficial to the internal consistency of the homogeneous region. Hence, we proposed a new approach that spatial information is brought into EMBoost clustering algorithm, which consisted of the adjacent pixels relative position and the neighbor texture distance, in order to improve the performance EMBoost clustering method. According to the experimental results of the texture image segmentation and the Synthetic Aperture Radar (SAR) image segmentation, the proposed method can obtain better accuracy and visual effect, compared against other methods.

Citation Download Citation

Shuiping Gou, Quanhua Fei, and Yifan Zhao "EMBoost clustering based on spatial information for image segmentation", Proc. SPIE 8003, MIPPR 2011: Automatic Target Recognition and Image Analysis, 800315 (8 December 2011); https://doi.org/10.1117/12.902084

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KEYWORDS

Expectation maximization algorithms

Image segmentation

Image processing algorithms and systems

Synthetic aperture radar

Data modeling

Detection and tracking algorithms

Mathematical modeling

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