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|Title:||Randomized locality sensitive vocabularies for bag-of-features model||Authors:||Mu, Y.
|Issue Date:||2010||Citation:||Mu, Y.,Sun, J.,Han, T.X.,Cheong, L.-F.,Yan, S. (2010). Randomized locality sensitive vocabularies for bag-of-features model. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6313 LNCS (PART 3) : 748-761. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-15558-1_54||Abstract:||Visual vocabulary construction is an integral part of the popular Bag-of-Features (BOF) model. When visual data scale up (in terms of the dimensionality of features or/and the number of samples), most existing algorithms (e.g. k-means) become unfavorable due to the prohibitive time and space requirements. In this paper we propose the random locality sensitive vocabulary (RLSV) scheme towards efficient visual vocabulary construction in such scenarios. Integrating ideas from the Locality Sensitive Hashing (LSH) and the Random Forest (RF), RLSV generates and aggregates multiple visual vocabularies based on random projections, without taking clustering or training efforts. This simple scheme demonstrates superior time and space efficiency over prior methods, in both theory and practice, while often achieving comparable or even better performances. Besides, extensions to supervised and kernelized vocabulary constructions are also discussed and experimented with. © 2010 Springer-Verlag.||Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||URI:||http://scholarbank.nus.edu.sg/handle/10635/71544||ISBN:||364215557X||ISSN:||03029743||DOI:||10.1007/978-3-642-15558-1_54|
|Appears in Collections:||Staff Publications|
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