Local feature selection for urban image retrieval

Abstract : 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
Type de document :
Communication dans un congrès
ISSCS 2017 : International Symposium on Signals, Circuits and Systems , Jul 2017, Iasi, Romania. IEEE Computer Society, Proceedings ISSCS 2017 : International Symposium on Signals, Circuits and Systems pp.1 - 4, 2017, 〈10.1109/ISSCS.2017.8034887〉
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https://hal.archives-ouvertes.fr/hal-01687246
Contributeur : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Soumis le : jeudi 18 janvier 2018 - 11:54:59
Dernière modification le : jeudi 31 mai 2018 - 09:12:02

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Nicolas Hascoet, Titus Zaharia. Local feature selection for urban image retrieval. ISSCS 2017 : International Symposium on Signals, Circuits and Systems , Jul 2017, Iasi, Romania. IEEE Computer Society, Proceedings ISSCS 2017 : International Symposium on Signals, Circuits and Systems pp.1 - 4, 2017, 〈10.1109/ISSCS.2017.8034887〉. 〈hal-01687246〉

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