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首页> 外文会议>European Conference on Computer Vision(ECCV 2006) pt.3; 20060507-13; Graz(AT) >Sparse Flexible Models of Local Features
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Sparse Flexible Models of Local Features

机译：局部特征的稀疏灵活模型

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摘要

In recent years there has been growing interest in recognition models using local image features for applications ranging from long range motion matching to object class recognition systems. Currently, many state-of-the-art approaches have models involving very restrictive priors in terms of the number of local features and their spatial relations. The adoption of such priors in those models are necessary for simplifying both the learning and inference tasks. Also, most of the state-of-the-art learning approaches are semi-supervised batch processes, which considerably reduce their suitability in dynamic environments, where unanno-tated new images are continuously presented to the learning system. In this work we propose: 1) a new model representation that has a less restrictive prior on the geometry and number of local features, where the geometry of each local feature is influenced by its k closest neighbors and models may contain hundreds of features; and 2) a novel unsuper-vised on-line learning algorithm that is capable of estimating the model parameters efficiently and accurately. We implement a visual class recognition system using the new model and learning method proposed here, and demonstrate that our system produces competitive classification and localization results compared to state-of-the-art methods. Moreover, we show that the learning algorithm is able to model not only classes with consistent texture (e.g., faces), but also classes with shape only (e.g., leaves), classes with a common shape but with a great variability in terms of internal texture (e.g., cups), and classes of flexible objects (e.g., snake).
机译：近年来，对于使用局部图像特征的识别模型的兴趣日益增长，其应用范围从远程运动匹配到对象类别识别系统。当前，许多最先进的方法都具有涉及局部特征数量及其空间关系的非常先验的模型。在那些模型中采用这种先验对简化学习和推理任务都是必要的。同样，大多数最新的学习方法都是半监督的批处理过程，这大大降低了它们在动态环境中的适用性，在动态环境中，未经注释的新图像不断呈现给学习系统。在这项工作中，我们提出以下建议：1）一种新的模型表示，其对局部特征的几何形状和数量具有较少的先验限制，其中每个局部特征的几何形状受其k个最近邻居影响，并且模型可能包含数百个特征； 2）一种新颖的无监督在线学习算法，能够高效，准确地估计模型参数。我们使用此处提出的新模型和学习方法实现了视觉类识别系统，并证明与最新方法相比，我们的系统产生了竞争性的分类和本地化结果。此外，我们表明学习算法不仅能够建模具有一致纹理的类（例如，面孔），而且还能够建模仅具有形状的类（例如，叶子），具有共同形状的类，但是在内部方面具有很大的可变性纹理（例如杯子）和柔性对象的类别（例如蛇）。

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