Abstract
In this study, a novel image segmentation algorithm based on watershed and kernel evolutionary clustering algorithm (WKECA) is proposed. An improved watershed algorithm, marker driven watershed transform, is used to segment image into many small regions and the image features of every region are extracted. By using kernel functions, the image features in the original space are mapped to a high-dimensional feature space, in which we can perform clustering efficiently on the unsupervised segmentation task. The proposed algorithm can be used to cope with different types of images, such as natural image, texture image and remote sensing image. The experimental results show that WKECA is competent for segmenting most of the testing images with high quality.
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Lei, C., Ma, J., Jiang, X. (2016). Unsupervised Image Segmentation Based on Watershed and Kernel Evolutionary Clustering Algorithm. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_3
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DOI: https://doi.org/10.1007/978-981-10-3614-9_3
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