Visual categorization with bags of keypoints
Visual categorization with bags of keypoints
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发表时间：
2002
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通讯作者：
Gabriella Csurka;C. Dance;Lixin Fan;J. Willamowski;Cédric Bray
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作者：
Gabriella Csurka;C. Dance;Lixin Fan;J. Willamowski;Cédric Bray
我们提出了一种新的方法，通用的视觉分类：识别自然图像的对象内容，同时概括的对象类固有的变化的问题。该关键点包方法基于图像块的仿射不变描述符的矢量量化。我们提出并比较了两种不同的实现使用不同的分类：朴素贝叶斯和SVM。该方法的主要优点是它是简单的，计算效率和本质不变。我们提出的结果，同时分类七个语义视觉类别。这些结果清楚地表明，该方法是强大的背景杂波，并产生良好的分类精度，即使没有利用几何信息。
We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors of image patches. We propose and compare two alternative implementations using different classifiers: Naive Bayes and SVM. The main advantages of the method are that it is simple, computationally efficient and intrinsically invariant. We present results for simultaneously classifying seven semantic visual categories. These results clearly demonstrate that the method is robust to background clutter and produces good categorization accuracy even without exploiting geometric information.