In this paper

a new natural image segmentation method based on Gestalt rules is proposed to reduce the effect of illumination and other factors

as well as improve segmentation accuracy. The novelty of the proposed method lies in three aspects: first

the original image is segmented into several subregions with the use of normalized cut algorithm to reduce the effects of illumination

background smooth

and other natural factors. Second

the Gestalt rules are introduced to measure the regions

and a quantitative calculation model based on the regions

which can obtain the ratio region and agrees with human visual perception

is proposed. Third

a new merge algorithm based on the ratio region is proposed. The final segmentation result is obtained by merging the regions through the simple and highly efficient merge algorithm. Quantitative and visual inspections of 30 images show the effectiveness of the Gestalt rules on image segmentation. The results of the comparative experiments show that the effect of the algorithm is more aligned with human visual perception and that our algorithm performs better than the comparative experiments overall in the evaluation index of probabilistic Rand index

variation of information

and global consistency error. The results of our algorithm are closer to the results of artificial labels. A natural image segmentation method based on Gestalt rules is proposed. The method adopts oversegmentation and measures the region by applying the Gestalt rules that can effectively reduce the effect of natural factors in the segmentation process and improve segmentation accuracy. Experimental results show that the proposed image segmentation algorithm based on Gestalt rules exhibits better efficiency and accuracy than other algorithms

but the effect is not good for natural image with some sharp and thin object.