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Learning Discriminative Mid-Level Patches for Fast Scene Classification

机译：学习用于快速场景分类的鉴别中级补丁

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摘要

Discriminative mid-level patch based approaches have become increasingly popular in the past few years. The reason of their popularity can be attributed to the fact that discriminative patches have the ability to accumulate low level features to form high level descriptors for objects and images. Unfortunately, state-of-the-art algorithms to discover those patches heavily rely on SVM related techniques, which consume a lot of computation resources in training. To overcome this shortage and apply discriminative part based techniques to more complicated computer vision problems with larger datasets, we proposed a fast, simple yet powerful way to mine part classifiers automatically with only class labels provided. Our experiments showed that our method, the Fast Exemplar Clustering, is 20 times faster than the commonly used SVM based methods while at the same time attaining competitive accuracy on scene classification.
机译：在过去几年中，基于中级贴片的方法越来越受欢迎。他们受欢迎程度的原因可以归因于判别贴片具有累积低级功能的能力，以形成对象和图像的高级描述符。不幸的是，最先进的算法发现这些补丁大量依赖于SVM相关技术，这消耗了训练中的大量计算资源。为了克服这种短缺并将基于识别部分的技术应用于更加复杂的计算机视觉问题与较大的数据集，我们提出了一种快速，简单而强大的方法，只能使用类标签自动挖掘零件分类器。我们的实验表明，我们的方法，快速示例聚类，比基于SVM的方法快20倍，同时实现了场景分类的竞争精度。

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