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A novel fast scene classification method via DCT

机译:基于DCT的新型快速场景分类方法

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Scene classification is a hot problem in the computer vision. In this paper, a novel rapid scene classification method is proposed based on the discrete cosine transform (DCT) domain. Firstly, we divided the whole image into several areas of the same size without repetition, do DCT transform with the size of the BB in each separate sub image area divided above. Secondly, scanning AC coefficients in each DCT block by three ways, modeling based on the correlation between AC coefficients in DCT block for extracting the feature vectors. Finally, with the feature vectors obtained previously, using one-vs-all Support Vector Machine train classifiers. Experimental results show that the proposed method is effective in image classification.
机译:场景分类是计算机视觉中的热点问题。本文提出了一种新的基于离散余弦变换域的快速场景分类方法。首先,我们将整个图像分为相同大小的几个区域而没有重复,对DCT进行变换,并在上面划分的每个单独的子图像区域中使用BB的大小。其次,通过三种方式扫描每个DCT块中的AC系数,基于DCT块中AC系数之间的相关性进行建模以提取特征向量。最终,使用先前获得的特征向量,使用“一对所有支持向量机”训练分类器。实验结果表明,该方法在图像分类中是有效的。

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