Fast Approximate Kernel-Based Similarity Search for Image Retrieval Task

David Gorisse 1 Matthieu Cord 2 Frédéric Precioso 1 Sylvie Philipp-Foliguet 1
1 MIDI - Multimedia Indexation and Data Integration
ETIS - Equipes Traitement de l'Information et Systèmes
2 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : In content based image retrieval, the success of any distance-based indexing scheme depends critically on the quality of the chosen distance metric. We propose in this paper a kernel-based similarity approach working on sets of vectors to represent images. We introduce a method for fast approximate similarity search in large image databases with our kernel-based similarity metric. We evaluate our algorithm on image retrieval task and show it to be accurate and faster than linear scanning.
Document type :
Conference papers
Complete list of metadatas
Contributor : Michel Jordan <>
Submitted on : Monday, January 14, 2013 - 11:49:54 AM
Last modification on : Friday, October 4, 2019 - 12:14:02 PM



David Gorisse, Matthieu Cord, Frédéric Precioso, Sylvie Philipp-Foliguet. Fast Approximate Kernel-Based Similarity Search for Image Retrieval Task. ICPR 2008 - 19th International Conference on Pattern Recognition, Dec 2008, Tampa, Florida, United States. pp.1-4, ⟨10.1109/ICPR.2008.4761225⟩. ⟨hal-00773556⟩



Record views