Paper Info
Title
Performance Evaluation Of Local Descriptors For Affine Invariant Region Detector
Abstract
Local feature descriptors are widely used in many computer vision applications. Over the past couple of decades, several local feature descriptors have been proposed which are robust to challenging conditions. Since they show different characteristics in different environment, it is necessary to evaluate their performance in an intensive and consistent manner. However, there has been no relevant work that addresses this problem, especially for the affine invariant region detectors which are popularly used in object recognition and classification. In this paper, we present a useful and rigorous performance evaluation of local descriptors for affine invariant region detector, in which MSER (maximally stable extremal regions) detector is employed. We intensively evaluate local patch based descriptors as well as binary descriptors, including SIFT (scale invariant feature transform), SURF (speeded up robust features), BRIEF (binary robust independent elementary features), FREAK (fast retina keypoint), Shape descriptor, and LIOP (local intensity order pattern). Intensive evaluation on standard dataset shows that LIOP outperforms the other descriptors in terms of precision and recall metric.
Year
DOI
Venue
2014
10.1007/978-3-319-16628-5_45
COMPUTER VISION - ACCV 2014 WORKSHOPS, PT I
Field
DocType
Volume
Scale-invariant feature transform,Computer vision,Affine shape adaptation,Harris affine region detector,Pattern recognition,Affine combination,Computer science,Precision and recall,Maximally stable extremal regions,Artificial intelligence,Hessian affine region detector,Cognitive neuroscience of visual object recognition
Conference
9008
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
19
2
Name
Order
Citations
PageRank
Man Hee Lee1778.18
In Kyu Park231635.97