@inproceedings{0563d32f8e924d52befa493e377a7256,
title = "WLIB-SIFT: A Distinctive Local Image Feature Descriptor",
abstract = "SIFT descriptor plays a great role in image mosaic, image retrieval and target recognition for good invariance of translation, rotation and zoom. However, the disadvantages of SIFT descriptor are its high dimensionality and complex computation. Besides, SIFT descriptor has poor performance when massive similar local features and complex background exist in the matching image. In this paper, a distinctive and robust weighted local intensity binary SIFT descriptor(WLIB-SIFT) is proposed. A WLIB-SIFT descriptor consists of a weighted binary SIFT descriptor(B-SIFT) and a weighted local intensity binary descriptor. The experimental results show that the WLIB-SIFT descriptor has better accuracy and faster speed than the SIFT descriptor. Compared with the B-SIFT descriptor, the WLIB-SIFT descriptor has better robustness and distinctiveness at almost the same speed.",
keywords = "B-SIFT, Feature descriptor, Image matching, SIFT, WLIB-SIFT",
author = "Mao Wei and Peng Xiwei",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2nd IEEE International Conference on Information Communication and Signal Processing, ICICSP 2019 ; Conference date: 28-09-2019 Through 30-09-2019",
year = "2019",
month = sep,
doi = "10.1109/ICICSP48821.2019.8958587",
language = "English",
series = "2019 2nd IEEE International Conference on Information Communication and Signal Processing, ICICSP 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "379--383",
booktitle = "2019 2nd IEEE International Conference on Information Communication and Signal Processing, ICICSP 2019",
address = "United States",
}