Multiple Object Class Detection with a Generative Model
Multiple Object Class Detection with a Generative Model
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DOI：
10.1109/cvpr.2006.202
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发表时间：
2006-06
期刊：
影响因子：
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通讯作者：
K. Mikolajczyk;B. Leibe;B. Schiele
中科院分区：
文献类型：
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作者：
K. Mikolajczyk;B. Leibe;B. Schiele
在本文中，我们提出了一种能够使用生成模型同时识别和定位多个对象类的方法。一种新颖的分层表示允许在单个，缩放和旋转不变的模型中表示单个图像以及各种对象类。该识别方法基于码本表示，其中基于边缘特征构建的外观聚类在多个对象类之间共享。概率模型允许在同一图像中可靠地检测各种物体。该方法采用快速聚类和匹配方法，能够处理数百万个高维特征，具有很高的效率。该系统在大范围尺度、平面内旋转、背景杂波和部分遮挡的几种目标类别上表现出优异的性能。所提出的多目标类检测方法的性能与专门用于单个目标类识别问题的最新方法具有竞争力。
In this paper we propose an approach capable of simultaneous recognition and localization of multiple object classes using a generative model. A novel hierarchical representation allows to represent individual images as well as various objects classes in a single, scale and rotation invariant model. The recognition method is based on a codebook representation where appearance clusters built from edge based features are shared among several object classes. A probabilistic model allows for reliable detection of various objects in the same image. The approach is highly efficient due to fast clustering and matching methods capable of dealing with millions of high dimensional features. The system shows excellent performance on several object categories over a wide range of scales, in-plane rotations, background clutter, and partial occlusions. The performance of the proposed multi-object class detection approach is competitive to state of the art approaches dedicated to a single object class recognition problem.