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

• We first propose to utilize the cosine interesting pattern to construct the noise filter.
• We then prove that to filter noise only requires to mine the shortest cosine interesting patterns, which dramatically simplifies the mining process.
• We present an in-breadth pruning technique to further speed up the mining process.

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.