面向电子商务图像识别的商品分割：方法优化与业务价值实现——基于加权有界Hessian选择性分割

Product Segmentation for E-Commerce Image Recognition: Method Optimization and Business Value Realization—Based on Weighted Bounded Hessian Selective Segmentation

摘要: 在电子商务应用中，商品图像往往包含复杂背景、光照不均及拍摄角度多变等问题，导致目标商品提取困难。为提升商品图像中目标区域的分割精度，本文首次将加权有界Hessian变分模型与测地距离先验引入电商图像分割任务中，提出一种适用于商品图像处理的选择性分割方法。通过自动估计一阶与二阶正则化权函数，自适应调节图像中边缘与区域的平滑强度，同时融合测地距离项提升对用户标注点的响应能力。在优化方面，本文采用交替方向乘子法(ADMM)对模型进行高效求解。实验以典型电子商务场景中的商品图像为基础，选取多种类别(如服饰、数码、家居用品)开展测试。实验结果表明，本文方法在目标轮廓完整性与边界平滑性上优势显著。该研究为电商平台的核心业务，如高精度商品搜索、视觉推荐系统及自动化主图生成，提供了可靠的底层技术支持，有望直接提升用户转化率并降低商家运营成本，具有明确的商业应用前景。

Abstract: In e-commerce applications, product images often suffer from complex backgrounds, uneven illumination, and diverse shooting angles, making accurate product extraction challenging. To improve the segmentation accuracy of target regions in product images, this paper introduces, for the first time, a weighted bounded Hessian variational model combined with a geodesic distance prior into the task of e-commerce image segmentation, proposing a selective segmentation method tailored for product image processing. By automatically estimating first- and second-order regularization weight functions, the method adaptively adjusts the smoothness intensity of edges and regions in the image, while incorporating a geodesic distance term to enhance responsiveness to user annotations. For optimization, the Alternating Direction Method of Multipliers (ADMM) is employed to efficiently solve the model. Experiments were conducted on product images from typical e-commerce scenarios, covering multiple categories such as apparel, electronics, and home goods. The results demonstrate that the proposed method significantly outperforms existing algorithms in terms of target contour integrity and boundary smoothness. This study provides reliable underlying technical support for core e-commerce operations, including high-precision product search, visual recommendation systems, and automated main image generation, with the potential to directly increase user conversion rates and reduce operational costs for merchants, highlighting its clear commercial applicability.