TY - GEN
T1 - Local feature selection for urban image retrieval
AU - Hascoet, Nicolas
AU - Zaharia, Titus
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/12
Y1 - 2017/9/12
N2 - In this paper, we propose an improved image retrieval method, dedicated to images of buildings/landmarks from urban environments. Locally detected key points are binary labelled as building or no-building using a SVM-based classifier. Thereafter, only key points labelled as building are retained. In this way, the data in the database vocabulary is reduced to only the relevant one and solely the relevant features, effectively describing the targeted buildings are considered. The experimental results, carried out on the Paris6k and Oxford5k data sets show significant improvement in terms of retrieval precision.
AB - In this paper, we propose an improved image retrieval method, dedicated to images of buildings/landmarks from urban environments. Locally detected key points are binary labelled as building or no-building using a SVM-based classifier. Thereafter, only key points labelled as building are retained. In this way, the data in the database vocabulary is reduced to only the relevant one and solely the relevant features, effectively describing the targeted buildings are considered. The experimental results, carried out on the Paris6k and Oxford5k data sets show significant improvement in terms of retrieval precision.
U2 - 10.1109/ISSCS.2017.8034887
DO - 10.1109/ISSCS.2017.8034887
M3 - Conference contribution
AN - SCOPUS:85032305508
T3 - ISSCS 2017 - International Symposium on Signals, Circuits and Systems
BT - ISSCS 2017 - International Symposium on Signals, Circuits and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Symposium on Signals, Circuits and Systems, ISSCS 2017
Y2 - 13 July 2017 through 14 July 2017
ER -