Abstract
Image representation is an important problem in the field of computer vision. The Non-symmetry and Anti-packing pattern representation Model (NAM) is an effective pattern representation model. In order to further improve the image segmentation quality and segmentation efficiency, in this paper, we improve the recently published NAMLab algorithm in two aspects. First, the CIEDE2000 color difference formula is used to replace the calculation formula of the color feature similarity in the original NAMLab algorithm. The formula is based on the human vision response to RGB and it is used to accurately represent the reception of the color. Secondly, the calculation formula of texture features in the original NAMLab algorithm is modified. The original NAMLab algorithm is based on the Weber Local Descriptor (WLD) texture descriptor to describe the feature texture of the image. In order to better meet the characteristics of human vision observation, we found that the Gabor wavelet is very similar to the stimulus response of simple cells in the human visual system, which is more in line with the characteristics of the human vision, so we choose Gabor filter as the feature texture description of the image. Finally, the improved algorithm is compared with the state-of-the-art algorithms in the field of image segmentation on six datasets, and it achieves better results in terms of visual presentation and the segmentation indicators.
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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, and the National Natural Science Foundation of China under Grant No. 61300134, the National Undergraduate Innovative and Entrepreneurial Training Program under Grant No. 202110561070 and No.202110561066.
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Zheng, Y., Qiu, S., Huang, J., Xu, Y., Zou, Z., Sun, P. (2022). An Improved NAMLab Algorithm Based on CIECDE2000 Color Difference Formula and Gabor Filter for Image Segmentation. 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_46
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