Graph Theory-based Fast Linear Iterative Clustering Multi-feature Fusion Model for Art Image Segmentation
Hongyu Lu · DOAJ (DOAJ: Directory of Open Access Journals) · 2022
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 images with complex backgrounds and uneven gray levels effectively. Especially, for the art images seg-mentation with high resolution, the distinguishment degree between the foreground of the object to be segmented 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 a better effect in terms of evaluation indexes (USE, BR, ASA, and Time).