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首页> 外文会议>European Conference on Computer Vision(ECCV 2004) pt.4; 20040511-20040514; Prague; CZ >Multiple Classifier System Approach to Model Pruning in Object Recognition
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Multiple Classifier System Approach to Model Pruning in Object Recognition

机译：目标识别中模型修剪的多分类器系统方法

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摘要

We propose a multiple classifier system approach to object recognition in computer vision. The aim of the approach is to use multiple experts successively to prune the list of candidate hypotheses that have to be considered for object interpretation. The experts are organised in a serial architecture, with the later stages of the system dealing with a monotonically decreasing number of models. We develop a theoretical model which underpins this approach to object recognition and show how it relates to various heuristic design strategies advocated in the literature. The merits of the advocated approach are then demonstrated experimentally using the SOIL database. We show how the overall performance of a two stage object recognition system, designed using the proposed methodology, improves. The improvement is achieved in spite of using a weak recogniser for the first (pruning) stage. The effects of different pruning strategies are demonstrated.
机译：我们提出了一种用于计算机视觉中目标识别的多分类器系统方法。该方法的目的是连续使用多个专家来修剪对象解释必须考虑的候选假设列表。专家以串行体系结构组织，系统的后期阶段处理模型数量的单调减少。我们开发了一种理论模型，该模型为该对象识别方法奠定了基础，并展示了它与文献中提倡的各种启发式设计策略之间的关系。然后使用SOIL数据库通过实验证明了所提倡方法的优点。我们展示了使用所提出的方法设计的两阶段目标识别系统的整体性能如何提高。尽管在第一（修剪）阶段使用了弱识别器，但仍可以实现改进。演示了不同修剪策略的效果。

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