University of Adelaide, Adelaide, SA 5005, Australia.
IEEE Trans Image Process. 2011 Jan;20(1):213-26. doi: 10.1109/TIP.2010.2053548. Epub 2010 Jun 21.
The ability to efficiently and accurately detect objects plays a very crucial role for many computer vision tasks. Recently, offline object detectors have shown a tremendous success. However, one major drawback of offline techniques is that a complete set of training data has to be collected beforehand. In addition, once learned, an offline detector cannot make use of newly arriving data. To alleviate these drawbacks, online learning has been adopted with the following objectives: 1) the technique should be computationally and storage efficient; 2) the updated classifier must maintain its high classification accuracy. In this paper, we propose an effective and efficient framework for learning an adaptive online greedy sparse linear discriminant analysis model. Unlike many existing online boosting detectors, which usually apply exponential or logistic loss, our online algorithm makes use of linear discriminant analysis' learning criterion that not only aims to maximize the class-separation criterion but also incorporates the asymmetrical property of training data distributions. We provide a better alternative for online boosting algorithms in the context of training a visual object detector. We demonstrate the robustness and efficiency of our methods on handwritten digit and face data sets. Our results confirm that object detection tasks benefit significantly when trained in an online manner.
对象的高效、准确检测对于许多计算机视觉任务至关重要。最近，离线对象检测器取得了巨大的成功。然而，离线技术的一个主要缺点是必须事先收集一整套训练数据。此外，一旦学习完成，离线检测器就无法利用新出现的数据。为了缓解这些缺点，已经采用了在线学习，其目标如下：1）该技术应具有计算和存储效率；2）更新的分类器必须保持其高分类准确性。在本文中，我们提出了一种用于学习自适应在线贪婪稀疏线性判别分析模型的有效且高效的框架。与许多现有的在线提升检测器不同，后者通常应用指数或逻辑损失，我们的在线算法利用了线性判别分析的学习准则，该准则不仅旨在最大化类别分离准则，而且还包含了训练数据分布的不对称性。我们在训练视觉对象检测器的上下文中为在线提升算法提供了更好的选择。我们在手写数字和人脸数据集上证明了我们的方法的鲁棒性和效率。我们的结果证实，在在线方式下训练对象检测任务会带来显著的收益。
