Binary Partition Trees for Object Detection
Binary Partition Trees for Object Detection
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DOI：
10.1109/tip.2008.2002841
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发表时间：
2008-11
影响因子：
10.6
通讯作者：
Verónica Vilaplana;F. Marqués;P. Salembier
中科院分区：
文献类型：
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作者：
Verónica Vilaplana;F. Marqués;P. Salembier
讨论了二叉划分树(BPT)在目标检测中的应用。BPT是图像的基于区域的分层表示。它们定义了覆盖图像支持并跨越各种分辨率级别的缩小区域集。由于它们极大地减少了搜索空间，因此对目标检测很有吸引力。本文对BPT用于目标检测的几个问题进行了研究。在树的构造方面，我们分析了在降低计算复杂度和精度之间的折衷。这将导致我们在BPT中定义两个部分：一个提供准确性，另一个表示对象检测任务的搜索空间。然后对构建树的各种相似性度量进行了客观的分析和比较。我们得出结论，对于BPT中提供精度的部分和定义搜索空间的部分，应该使用不同的相似标准，并针对每种情况提出了具体的标准。然后讨论了基于BPT的目标检测策略。提出并讨论了节点扩展的概念。最后，给出了几个目标检测的例子，说明了该方法的通用性和有效性。
This paper discusses the use of binary partition trees (BPTs) for object detection. BPTs are hierarchical region-based representations of images. They define a reduced set of regions that covers the image support and that spans various levels of resolution. They are attractive for object detection as they tremendously reduce the search space. In this paper, several issues related to the use of BPT for object detection are studied. Concerning the tree construction, we analyze the compromise between computational complexity reduction and accuracy. This will lead us to define two parts in the BPT: one providing accuracy and one representing the search space for the object detection task. Then we analyze and objectively compare various similarity measures for the tree construction. We conclude that different similarity criteria should be used for the part providing accuracy in the BPT and for the part defining the search space and specific criteria are proposed for each case. Then we discuss the object detection strategy based on BPT. The notion of node extension is proposed and discussed. Finally, several object detection examples illustrating the generality of the approach and its efficiency are reported.