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.
|Original language||English (US)|
|Title of host publication||Computer Vision, ECCV 2010 - 11th European Conference on Computer Vision, Proceedings|
|Number of pages||14|
|ISBN (Print)||364215557X, 9783642155574|
|State||Published - 2010|
|Event||11th European Conference on Computer Vision, ECCV 2010 - Heraklion, Crete, Greece|
Duration: Sep 10 2010 → Sep 11 2010
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||11th European Conference on Computer Vision, ECCV 2010|
|Period||9/10/10 → 9/11/10|
Bibliographical noteFunding Information:
Support of IDMPO Grant R-705-000-018-279 Singapore and NRF/IDM Program under research Grant NRF2008IDMIDM004-029 are gratefully acknowledged.