Multiple Measurements and Joint Dimensionality Reduction for Large Scale Image Search with Short Vectors

Abstract : This paper addresses the construction of a short-vector (128D) image representation for large-scale image and particular object retrieval. In particular, the method of joint dimensionality reduction of multiple vocabularies is considered. We study a variety of vocabulary generation techniques: different k-means initializations, different descriptotr transformations, different measurement regions for descriptor extraction. Our extensive evaluation shows that different combinations of vocabularies, each partitioning the descriptor space in a different yet complementary manner, results in a significant performance improvement, which exceeds the state-of-the-art.
Type de document :
Communication dans un congrès
ICMR 2015 - International Conference on Multimedia Retrieval, Jun 2015, Shanghai, China. ACM Press, pp.1-4, 〈10.1145/2671188.2749366〉
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https://hal.inria.fr/hal-01842288
Contributeur : Teddy Furon <>
Soumis le : mercredi 18 juillet 2018 - 10:13:13
Dernière modification le : jeudi 15 novembre 2018 - 11:59:01

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Filip Radenović, Hervé Jégou, Ondrej Chum. Multiple Measurements and Joint Dimensionality Reduction for Large Scale Image Search with Short Vectors. ICMR 2015 - International Conference on Multimedia Retrieval, Jun 2015, Shanghai, China. ACM Press, pp.1-4, 〈10.1145/2671188.2749366〉. 〈hal-01842288〉

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