Saliency based SIFT keypoints filtration

Xin He; Huiyun Jing; Xuefeng Bai; Li Li; Qi Han; Xiamu Niu

doi:10.1117/12.946105

2 June 2012 Saliency based SIFT keypoints filtration

Xin He, Huiyun Jing, Xuefeng Bai, Li Li, Qi Han, Xiamu Niu

Proceedings Volume 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012); 833415 (2012) https://doi.org/10.1117/12.946105
Event: Fourth International Conference on Digital Image Processing (ICDIP 2012), 2012, Kuala Lumpur, Malaysia

Abstract

In this paper, we propose a novel method to filter the keypoints and reduce redundant keypoints. SIFT (Scale Invariant Feature Transform) is one of the most robust and widely used methods for image matching and object recognition, which is robust to illumination changes, image scaling and rotation. However SIFT generates a large number of redundant keypoints in the background of the scene. Based on saliency detection and salient region selection, the keypoints out of the selected salient region are pruned in our method. The experimental results show that though the repeatability in our method is a little lower than original SIFT (less than 6%), the number of keypoints in our method is significantly reduced (more than 33%).

Citation Download Citation

Xin He, Huiyun Jing, Xuefeng Bai, Li Li, Qi Han, and Xiamu Niu "Saliency based SIFT keypoints filtration", Proc. SPIE 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012), 833415 (2 June 2012); https://doi.org/10.1117/12.946105

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KEYWORDS

Visual process modeling

Visualization

Image segmentation

Digital image processing

Image compression

Object recognition

Computer science

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