Abstract
How to effectively segment an image into the non-overlapping sub-regions and make the segmentation results conform to the perception of the human vision have always been a key issue in the field of computer vision. Extensive studies have proved that the CIEDE2000 formula is the most consistent method with the color space distribution for objects recognized by human perspective. The representation system of the NAMLab algorithm ignored the differences of the pixels at different positions of a region, and produced information loss in the process of dimensionality reduction. Therefore, in this paper, we propose an improved NAMLab algorithm based on the Earth Moving Distance (EMD) and the CIEDE2000 color difference formula. First, a K-means clustering is performed on the Lab features of all the pixels in the region in order to obtain the region color feature histogram. Then, the CIEDE2000 formula is used to calculate the differences between the cluster centers of the adjacent region color histograms. Finally, the EMD algorithm is used to calculate the color similarity among regions. When compared with the state-of-the-art algorithms, the experimental results presented in this paper demonstrate that the proposed algorithm has better segmentation performance, and the obtained segmentation results are more in line with the perception of the human vision.
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Acknowledgment
This work is supported by the Natural Science Foundation of Guangdong Province of China under Grant No. 2017A030313349 and No. 2021A1515011517, the National Natural Science Foundation of China under Grant No. 61300134, and the National Undergraduate Innovative and Entrepreneurial Training Program under Grant No. 202110561070 and No. 202110561066.
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Zheng, Y. et al. (2022). An Improved NAMLab Image Segmentation Algorithm Based on the Earth Moving Distance and the CIEDE2000 Color Difference Formula. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13393. Springer, Cham. https://doi.org/10.1007/978-3-031-13870-6_45
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