目的 似物性推荐为近年来提出的一种快速物体定位方法，而参数最小割模型作为似物性推荐的一种重要模型受到广泛关注。针对传统的参数最小割模型受颜色分布影响较大的问题，提出融合多个具有信息互补作用的外形先验予以改进。方法 首先构造了一种数据驱动的基于形状共享的外形先验，以发现具有相似外形的物体区域；其次，从格式塔完形心理学的角度入手，引出了一种测地星形凸面性的外形先验，约束外形的拓扑结构，生成外形不同的物体区域；最后，结合外形先验、颜色分布、边缘响应强度以及尺度线索，构建能量函数以表征新的模型，从而增强模型对复杂颜色分布的鲁棒性。结果 分别在Seg VOC12和BSDS300数据集中进行了外形先验有效性验证、复杂颜色分布下算法鲁棒性分析和前沿似物性推荐算法对比分析等实验，结果表明，本文采用融合互补性外形先验能提高候选区域定位精度，具有更好的颜色分布鲁棒性，当颜色简单性位于[0.7，,08]之间时，算法结合外形先验后平均最佳重叠率最高可达到9.8%的提升，且在与13种具有代表性的似物性推荐算法进行区域级物体定位能力对比实验中，本文算法在不同的重叠率阈值下均达到了相近的查全率。结论 本文算法具有更高的前景与背景的区分能力，能够适应各种复杂颜色分布，同时具有较好的物体定位能力。

Objective 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. Method 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. Result 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. Conclusion 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.