Object Detection with Discriminatively Trained Part-Based Models
Object Detection with Discriminatively Trained Part-Based Models
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DOI：
10.1109/tpami.2009.167
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发表时间：
2010-09-01
影响因子：
23.6
通讯作者：
Ramanan, Deva
中科院分区：
文献类型：
--
作者：
Felzenszwalb, Pedro F.;Girshick, Ross B.;Ramanan, Deva
我们描述了一个目标检测系统的基础上的混合物的多尺度可变形的部分模型。我们的系统能够代表高度可变的对象类，并实现了最先进的结果在PASCAL对象检测的挑战。虽然可变形零件模型已经变得相当流行，但它们的价值尚未在PASCAL数据集等困难的基准上得到证明。我们的系统依赖于使用部分标记数据进行区分训练的新方法。我们结合联合收割机的边缘敏感的方法，我们称之为潜在的SVM的形式主义的数据挖掘硬负的例子。潜在SVM是MI-SVM在潜在变量方面的重新表述。一个潜在的支持向量机是凸的，一旦为正例指定了潜在信息，训练问题就变成凸的。这导致了一种迭代训练算法，该算法在固定正例的潜在值和优化潜在SVM目标函数之间交替。
We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL data sets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin-sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM is a reformulation of MI-SVM in terms of latent variables. A latent SVM is semiconvex, and the training problem becomes convex once latent information is specified for the positive examples. This leads to an iterative training algorithm that alternates between fixing latent values for positive examples and optimizing the latent SVM objective function.