Efficient bag-of-feature kernel representation for image similarity search

Frédéric Precioso 1 Matthieu Cord 2 David Gorisse 1 Nicolas Thome 2
1 MIDI
ETIS - Equipes Traitement de l'Information et Systèmes
2 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Although “Bag-of-Features” image models have shown very good potential for object matching and image retrieval, such a complex data representation requires computationally expensive similarity measure evaluation. In this paper, we propose a framework unifying dictionary-based and kernel-based similarity functions that highlights the tradeoff between powerful data representation and eff cient similarity computation. On the basis of this formalism, we propose a new kernel-based similarity approach for Bag-of-Feature descriptions. We introduce a method for fast similarity search in large image databases. The conducted experiments prove that our approach is very competitive among State-of-the-art methods for similarity retrieval tasks.
Document type :
Conference papers
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-00773095
Contributor : Michel Jordan <>
Submitted on : Friday, January 11, 2013 - 3:47:07 PM
Last modification on : Thursday, March 21, 2019 - 2:32:33 PM

Identifiers

Citation

Frédéric Precioso, Matthieu Cord, David Gorisse, Nicolas Thome. Efficient bag-of-feature kernel representation for image similarity search. ICIP 2011 - IEEE International Conference on Image Processing, Sep 2011, Bruxelles, Belgium. pp.109-112, ⟨10.1109/ICIP.2011.6115618⟩. ⟨hal-00773095⟩

Share

Metrics

Record views

197