Details
| Original language | English |
|---|---|
| Pages (from-to) | 61-66 |
| Number of pages | 6 |
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Volume | 40 |
| Issue number | 3 |
| Publication status | Published - 14 Aug 2014 |
| Event | ISPRS Technical Commission III Symposium 2014 - Zurich, Switzerland Duration: 5 Sept 2014 → 7 Sept 2014 |
Abstract
This paper presents a new and fast binary descriptor for image matching learned from Haar features. The training uses AdaBoost; the weak learner is built on response function for Haar features, instead of histogram-type features. The weak classifier is selected from a large weak feature pool. The selected features have different feature type, scale and position within the patch, having correspond threshold value for weak classifiers. Besides, to cope with the fact in real matching that dissimilar matches are encountered much more often than similar matches, cascaded classifiers are trained to motivate training algorithms see a large number of dissimilar patch pairs. The final trained output are binary value vectors, namely descriptors, with corresponding weight and perceptron threshold for a strong classifier in every stage. We present preliminary results which serve as a proof-of-concept of the work.
Keywords
- AdaBoost, Descriptor learning, Haar features, Image descriptors, Image matching, Pooling configuration
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Geography, Planning and Development
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In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 40, No. 3, 14.08.2014, p. 61-66.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Learning image descriptors for matching based on Haar features
AU - Chen, L.
AU - Rottensteiner, F.
AU - Heipke, C.
PY - 2014/8/14
Y1 - 2014/8/14
N2 - This paper presents a new and fast binary descriptor for image matching learned from Haar features. The training uses AdaBoost; the weak learner is built on response function for Haar features, instead of histogram-type features. The weak classifier is selected from a large weak feature pool. The selected features have different feature type, scale and position within the patch, having correspond threshold value for weak classifiers. Besides, to cope with the fact in real matching that dissimilar matches are encountered much more often than similar matches, cascaded classifiers are trained to motivate training algorithms see a large number of dissimilar patch pairs. The final trained output are binary value vectors, namely descriptors, with corresponding weight and perceptron threshold for a strong classifier in every stage. We present preliminary results which serve as a proof-of-concept of the work.
AB - This paper presents a new and fast binary descriptor for image matching learned from Haar features. The training uses AdaBoost; the weak learner is built on response function for Haar features, instead of histogram-type features. The weak classifier is selected from a large weak feature pool. The selected features have different feature type, scale and position within the patch, having correspond threshold value for weak classifiers. Besides, to cope with the fact in real matching that dissimilar matches are encountered much more often than similar matches, cascaded classifiers are trained to motivate training algorithms see a large number of dissimilar patch pairs. The final trained output are binary value vectors, namely descriptors, with corresponding weight and perceptron threshold for a strong classifier in every stage. We present preliminary results which serve as a proof-of-concept of the work.
KW - AdaBoost
KW - Descriptor learning
KW - Haar features
KW - Image descriptors
KW - Image matching
KW - Pooling configuration
UR - http://www.scopus.com/inward/record.url?scp=84924276401&partnerID=8YFLogxK
U2 - 10.5194/isprsarchives-XL-3-61-2014
DO - 10.5194/isprsarchives-XL-3-61-2014
M3 - Conference article
AN - SCOPUS:84924276401
VL - 40
SP - 61
EP - 66
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SN - 1682-1750
IS - 3
T2 - ISPRS Technical Commission III Symposium 2014
Y2 - 5 September 2014 through 7 September 2014
ER -