A novel noise filter based on interesting pattern mining for bag-of-features images

A novel noise filter based on interesting pattern mining for bag-of-features images
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一种基于特征袋图像有趣模式挖掘的新型噪声滤波器

DOI:
10.1016/j.eswa.2013.07.058
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发表时间:
2013-12
影响因子:
8.5
通讯作者:
Zhuang Yi
Zhuang Yi
中科院分区:
计算机科学1区
文献类型:
--
作者:
Wu Zhiang;Cao Jie;Tao Haicheng;Zhuang Yi

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通过噪声滤波来提高图像数据的质量一直受到人们的关注。迄今为止,对图像内部噪声的滤波研究较多,而对正常图像中实例级噪声的滤波研究较少。本文旨在为特征袋图像提供噪声滤波器,(1)首先提出利用余弦感兴趣模式构建噪声滤波器;(2)然后证明了滤波噪声只需要挖掘最短余弦兴趣模式,极大地简化了挖掘过程;(3)提出了一种宽内剪枝技术,进一步加快了挖掘过程。在两个真实图像数据集上的实验结果证明了噪声滤波方法的有效性和高效性。
Improving the quality of image data through noise filtering has gained more attention for a long time. To date, many studies have been devoted to filter the noise inside the image, while few of them focus on filtering the instance-level noise among normal images. In this paper, aiming at providing a noise filter for bag-of-features images, (1) we first propose to utilize the cosine interesting pattern to construct the noise filter; (2) then we prove that to filter noise only requires to mine the shortest cosine interesting patterns, which dramatically simplifies the mining process; (3) we present an in-breadth pruning technique to further speed up the mining process. Experimental results on two real-life image datasets demonstrate effectiveness and efficiency of our noise filtering method.
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