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- 2016
基于低尺度词袋模型的图像快速分类方法
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Abstract:
提出一种新的框架用于改进传统词袋模型效率较低的问题。该方法建立在通过小波变换获取的低尺度图像表示上，利用在低尺度图像上提取单尺度的SIFT特征，建立低尺度视觉词典。由于大幅度减少了图像初始特征维数，该方法可以快速建立视觉词典，并且有效地降低后续图像分类所花费的时间。通过对Caltech101数据集全部8 677张图像的分类测试显示，该方法可以在保证分类性能的同时，有效地提升基于传统词袋模型的图像分类效率。实验结果表明，该方法可以全面提升金字塔匹配的词袋模型分类性能和分类效率，普遍用于传统词袋模型及其衍生方法。
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