Object proposal is a rapid object localization method proposed in recent years. Parametric min-cut model is one of the important models for object proposal. However

the existing parametric min-cut model has poor robustness for color distribution. Therefore

this study proposes an improved parametric min-cut model based on complementary shape prior. First

a data-driven shape sharing-based shape prior is combined to find object regions with a similar shape. Second

from the perspective of Gestalt psychology

the model is combined with geodesic star convexity to constrain the topology of the region shape for different object regions. Third

the shape prior

color distribution

edge response

and scale cue are combined to represent a robust model for color distribution. This study conducts various experiments in Seg VOC12 and BSDS300 datasets to verify the effectiveness of the shape prior

robustness of the algorithm under complex color distribution

and contrast analysis of state-of-the-art algorithms. Experimental results show that the proposed algorithm can improve the positioning accuracy of the target region and demonstrates good color distribution robustness. When color easiness is located [0.7

0.8]

the test results show that the average intersection-over-union (IoU) overlap rate can achieve a 9.8% increase. The comparative experiments with 13 typical object proposal algorithms show that the proposed algorithm can reach a similar recall ratio in different IoU overlap thresholds. The proposed algorithm can distinguish between foreground and background and adapt to various complex color distributions. The algorithm is good at object localization and other mainstream methods of object proposal.