Informative patches sampling for image classification by utilizing bottom-up and top-down information
Informative patches sampling for image classification by utilizing bottom-up and top-down information
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DOI：
10.1007/s00138-012-0473-x
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发表时间：
2012-02
影响因子：
3.3
通讯作者：
Shuang Bai;Tetsuya Matsumoto;Y. Takeuchi;H. Kudo;N. Ohnishi
中科院分区：
文献类型：
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作者：
Shuang Bai;Tetsuya Matsumoto;Y. Takeuchi;H. Kudo;N. Ohnishi
在基于视觉词袋框架的图像分类中，用于创建图像表示的图像块显着影响分类性能。然而，目前，补丁的采样主要基于处理低级图像信息或者只是定期或随机提取。这些方法并不有效，因为通过这些方法提取的补丁不一定能够区分图像分类。在本文中，我们建议通过处理低级图像信息来利用自下而上的信息，并通过探索训练图像网格的统计特性来利用自上而下的信息来提取图像块。在所提出的工作中，输入图像被划分为规则网格，每个网格都根据其自下而上的信息和/或自上而下的信息进行评估。随后，根据评估结果为每个网格分配一个显着性值，从而可以为图像创建显着性图。最后，根据获得的显着性图对输入图像进行补丁采样。此外，我们提出了一种融合这两种信息的方法。所提出的方法在对象类别和场景类别上进行评估。实验结果证明了其有效性。
In image classification based on bag of visual words framework, image patches used for creating image representations affect the classification performance significantly. However, currently, patches are sampled mainly based on processing low-level image information or just extracted regularly or randomly. These methods are not effective, because patches extracted through these approaches are not necessarily discriminative for image categorization. In this paper, we propose to utilize both bottom-up information through processing low-level image information and top-down information through exploring statistical properties of training image grids to extract image patches. In the proposed work, an input image is divided into regular grids, each of which is evaluated based on its bottom-up information and/or top-down information. Subsequently, every grid is assigned a saliency value based on its evaluation result, so that a saliency map can be created for the image. Finally, patch sampling from the input image is performed on the basis of the obtained saliency map. Furthermore, we propose a method to fuse these two kinds of information. The proposed methods are evaluated on both object categories and scene categories. Experiment results demonstrate their effectiveness.