摘要：

单类分类(OCC)技术如支撑向量数据描述(SVDD)和单分类支持向量机(OCSVM)，已在计算机视觉、机器学习和生物特征识别等应用领域中获得了广泛关注。由于目前大多数的OCC模型均是基于L2范数设计的，存在解不够稀疏、噪声敏感、需要二阶以上优化等问题，难以胜任有实时性需求的目标检测任务。针对这一问题，采用L1范数取代了OCSVM中L2范数的间隔项，提出一种基于L1-OCSVM的单类分类器。上述的取代不仅继承了SVM的大间隔原理，而且导出的优化问题是一阶的。然而，由于L1范数的引入，非线性L1-OCSVM模型中的特征样本不如L2范数总可以成对出现，因而也无法使用L2范数的内积替换。提供一种等价优化策略，即直接最小化变量的L1范数项，因而获得的解极其稀疏，非常有利于实时检测。针对林业问题中的非刚性目标检测，如林火、林烟和树冠等，在无人机航拍图像和地面遥感图像上进行实验，结果验证了L1-OCSVM在目标检测准确率、稀疏性和实时检测方面的显著优势。

关键词: 单类分类, L1范数, 实时性, 目标检测, 非刚性目标

Abstract:

One-Class Classification (OCC) techniques, such as Support Vector Domain Description (SVDD) and One Class Support Vector Machines (OCSVM), have received widespread attention in application fields such as computer vision, machine learning, and biometric recognition. Most current OCC models are designed based on the L2 norm; therefore, issues such as insufficient sparse solutions, noise sensitivity, and the need for second-order or higher optimization persist, making real-time object detection difficult. To address this issue, this study proposes a one-class classifier called L1-OCSVM by replacing the interval term of OCSVM′s L2 norm with the L1 norm. This substitution not only inherits the large interval principle of Support Vector Machines (SVM) but also leads to first-order optimization problems. However, owing to the introduction of the L1 norm, the feature samples in the nonlinear L1-OCSVM model no longer appear in pairs like the L2 norm, and therefore they cannot be replaced by the inner product of the L2 norm. Thus, an equivalent optimization strategy is provided, which directly minimizes the L1 norm term of the variable, resulting in extremely sparse solutions that are very conducive to real-time detection. Experiments on non-rigid object detection in forestry problems, such as forest fire, forest smoke, and tree crowns, using unmanned aerial vehicle images and ground remote sensing images verify the advantages of L1-OCSVM in object detection accuracy, sparsity, and real-time detection.

Key words: One-Class Classification (OCC), L1 norm, real time performance, object detection, non-rigid objects