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A Performance Evaluation of Local Descriptors
October 2005 (vol. 27 no. 10)
pp. 1615-1630
ASCII Text
x 
 
Krystian Mikolajczyk, Cordelia Schmid, "A Performance Evaluation of Local Descriptors," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1615-1630, October, 2005.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2005.188,
author = {Krystian Mikolajczyk and Cordelia Schmid},
title = {A Performance Evaluation of Local Descriptors},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {27},
number = {10},
issn = {0162-8828},
year = {2005},
pages = {1615-1630},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.188},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - A Performance Evaluation of Local Descriptors
IS - 10
SN - 0162-8828
SP1615
EP1630
EPD - 1615-1630
A1 - Krystian Mikolajczyk,
A1 - Cordelia Schmid,
PY - 2005
KW - Index Terms- Local descriptors
KW - interest points
KW - interest regions
KW - invariance
KW - matching
KW - recognition.
VL - 27
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
In this paper, we compare the performance of descriptors computed for local interest regions, as, for example, extracted by the Harris-Affine detector [32]. Many different descriptors have been proposed in the literature. It is unclear which descriptors are more appropriate and how their performance depends on the interest region detector. The descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the detector. Our evaluation uses as criterion recall with respect to precision and is carried out for different image transformations. We compare shape context [3], steerable filters [12], PCA-SIFT [19], differential invariants [20], spin images [21], SIFT [26], complex filters [37], moment invariants [43], and cross-correlation for different types of interest regions. We also propose an extension of the SIFT descriptor and show that it outperforms the original method. Furthermore, we observe that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best. Moments and steerable filters show the best performance among the low dimensional descriptors.

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Index Terms:
Index Terms- Local descriptors, interest points, interest regions, invariance, matching, recognition.
Citation:
Krystian Mikolajczyk, Cordelia Schmid, "A Performance Evaluation of Local Descriptors," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1615-1630, Oct. 2005, doi:10.1109/TPAMI.2005.188
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