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首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >Unsupervised Category Modeling, Recognition, and Segmentation in Images
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Unsupervised Category Modeling, Recognition, and Segmentation in Images

机译：图像中的无监督类别建模，识别和分割

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摘要

Suppose a set of arbitrary (unlabeled) images contains frequent occurrences of 2D objects from an unknown category. This paper is aimed at simultaneously solving the following related problems: 1) unsupervised identification of photometric, geometric, and topological properties of multiscale regions comprising instances of the 2D category, 2) learning a region-based structural model of the category in terms of these properties, and 3) detection, recognition, and segmentation of objects from the category in new images. To this end, each image is represented by a tree that captures a multiscale image segmentation. The trees are matched to extract the maximally matching subtrees across the set, which are taken as instances of the target category. The extracted subtrees are then fused into a tree union that represents the canonical category model. Detection, recognition, and segmentation of objects from the learned category are achieved simultaneously by finding matches of the category model with the segmentation tree of a new image. Experimental validation on benchmark data sets demonstrates the robustness and high accuracy of the learned category models when only a few training examples are used for learning without any human supervision.
机译：假设一组任意（未标记）图像包含频繁出现的来自未知类别的2D对象。本文旨在同时解决以下相关问题：1）无监督地识别包括2D类实例的多尺度区域的光度，几何和拓扑特性，2）在这些方面学习基于区域的结构模型属性，以及3）检测，识别和分割新图像中类别中的对象。为此，每个图像由捕获多尺度图像分割的树表示。匹配这些树以提取整个集合中最大匹配的子树，这些子树被视为目标类别的实例。然后将提取的子树融合到代表规范类别模型的树联合中。通过找到类别模型与新图像的分割树的匹配，可以同时实现对学习类别中对象的检测，识别和分割。在仅使用几个训练示例进行学习而无需任何人工监督的情况下，对基准数据集的实验验证证明了所学习类别模型的鲁棒性和高精度。

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