具有拒绝分类法的范例支持向量机的索引集成

Federico Becattini, Lorenzo Seidenari, A. Bimbo
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引用次数: 1

摘要

范例支持向量机的集合已被用于各种各样的任务,如对象检测、分割、标签转移和中级特征学习。为了使该技术有效,需要大量的分类器集合,这通常会使评估阶段变得令人望而却步。为了克服这个问题,我们利用样本分类器分数的联合分布来构建一个能够索引每个样本支持向量机的分类法,并能够快速评估整个集合。我们在Pascal 2007基准测试中对目标检测任务和简单分割任务进行了实验,以验证参考标准集成的索引数据结构的鲁棒性。我们还引入了一种拒绝策略来丢弃不相关的图像补丁,以便更有效地访问数据。
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Indexing Ensembles of Exemplar-SVMs with rejecting taxonomies
Ensembles of Exemplar-SVMs have been used for a wide variety of tasks, such as object detection, segmentation, label transfer and mid-level feature learning. In order to make this technique effective though a large collection of classifiers is needed, which often makes the evaluation phase prohibitive. To overcome this issue we exploit the joint distribution of exemplar classifier scores to build a taxonomy capable of indexing each Exemplar-SVM and enabling a fast evaluation of the whole ensemble. We experiment with the Pascal 2007 benchmark on the task of object detection and on a simple segmentation task, in order to verify the robustness of our indexing data structure with reference to the standard Ensemble. We also introduce a rejection strategy to discard not relevant image patches for a more efficient access to the data.
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