Genetic algorithm based image binarization approach and its quantitative evaluation via pooling

Huijun Hu; Ya Liu; Maofu Liu

doi:10.1117/12.2204902

17 December 2015 Genetic algorithm based image binarization approach and its quantitative evaluation via pooling

Huijun Hu, Ya Liu, Maofu Liu

Proceedings Volume 9811, MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis; 98110P (2015) https://doi.org/10.1117/12.2204902
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

The binarized image is very critical to image visual feature extraction, especially shape feature, and the image binarization approaches have been attracted more attentions in the past decades. In this paper, the genetic algorithm is applied to optimizing the binarization threshold of the strip steel defect image. In order to evaluate our genetic algorithm based image binarization approach in terms of quantity, we propose the novel pooling based evaluation metric, motivated by information retrieval community, to avoid the lack of ground-truth binary image. Experimental results show that our genetic algorithm based binarization approach is effective and efficiency in the strip steel defect images and our quantitative evaluation metric on image binarization via pooling is also feasible and practical.

Citation Download Citation

Huijun Hu, Ya Liu, and Maofu Liu "Genetic algorithm based image binarization approach and its quantitative evaluation via pooling", Proc. SPIE 9811, MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis, 98110P (17 December 2015); https://doi.org/10.1117/12.2204902

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