Title :
B-SIFT: A Binary SIFT Based Local Image Feature Descriptor
Author_Institution :
State-Province Joint Lab. of Digital Home Interactive Applic., Sun Yat-sen Univ., Guangzhou, China
Abstract :
Local feature point detection and description are the basis for Computer Vision. SIFT is one of the most efficient local image descriptors and have been well studied in recent years. In this paper, we introduce B-SIFT, a novel binary local image descriptor which is based with SIFT. The method is compared with SIFT, and is shown that B-SIFT is better both in accuracy and efficiency.
Keywords :
computer vision; feature extraction; object detection; transforms; B-SIFT; binary SIFT based local image feature descriptor; computer vision; image recognition; image registration; local feature point detection; scale invariant feature transform; visual tracking; Accuracy; Computer vision; Feature extraction; Histograms; Image retrieval; Vectors; Visualization; SIFT descriptor; binary image descriptor; image matching;
Conference_Titel :
Digital Home (ICDH), 2012 Fourth International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
978-1-4673-1348-3
DOI :
10.1109/ICDH.2012.69
