Title :
Evaluating the quality of individual SIFT features
Author :
Hui Su ; Wei-Hong Chuang ; Wenjun Lu ; Min Wu
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Maryland, College Park, MD, USA
fDate :
Sept. 30 2012-Oct. 3 2012
Abstract :
Scale-Invariant Feature Transform (SIFT) is one of the most popular local image features that are widely used in computer vision, image processing and image retrieval. In this paper we study the relation between the SIFT descriptor and its matching accuracy. We propose a method to quantitatively assess the quality of a SIFT feature descriptor in terms of robustness and discriminability. This would enable us to gain a better understanding of the strength and limitations of SIFT in emerging applications of SIFT-based image hash, and also to improve matching accuracy and efficiency in applications such as object search. The experimental results demonstrate the effectiveness of the proposed method.
Keywords :
computer vision; feature extraction; image matching; image retrieval; SIFT features; computer vision; image hash; image processing; image retrieval; local image features; matching accuracy; scale-invariant feature transform; Accuracy; Computer vision; Estimation; Histograms; Robustness; Training; Vectors; SIFT; feature matching; feature quality; vector quantization;
Conference_Titel :
Image Processing (ICIP), 2012 19th IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4673-2534-9
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2012.6467375
