Hongyu Lu This email address is being protected from spambots. You need JavaScript enabled to view it.

1Henan Institute of Economics and Trade Zhengzhou 450000, China


Received: September 7, 2021
Accepted: October 6, 2021
Publication Date: November 1, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202208_25(4).0005  


Image segmentation is the pre-processing stage of image analysis. It divides the image into different re-gions for the subsequent image analysis. The traditional segmentation algorithms can not deal with the images with complex background and uneven gray level effectively. Especially, for the art images seg-mentation with high resolution, the distinguishment degree between foreground of the object to be seg-mented and the background is small, which can lead to the incomplete segmentation effect. Therefore, this paper proposes a graph theorybased Fast Linear Iterative Clustering (FLIC) multi-feature fusion model for art image segmentation. First, the FLIC segmentation algorithm is used to pre-segment the su-perpixel of raw image. Second, the HOG feature, Lab color feature and spatial location feature are ex-tracted. Third, A multi-feature fusion strategy based on superpixel is designed. Four, the fast image seg-mentation based on multi-feature fusion is realized by using the graph theory. The graph theory is opti-mized by time domain convolution. Finally, the comparison experiments with other state-of-the-art methods are conducted on the public datasets: Berkeley Segmentation Dataset and Culture Gene Online . The results show that the proposed algorithm has better effect in terms of evaluation indexes (USE, BR, ASA and Time).

Keywords: art image segmentation, graph theory, FLIC, multi-feature fusion, time domain convolution


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