Bag-of-Words (BoW) model has been widely used for feature representation in multimedia search area, in which a key step is to vector-quantize local image descriptors and generate a visual vocabulary. Popular visual vocabulary construction schemes generally perform a flat or hierarchical clustering operation using a very large training set in their original description space. However, these methods usually suffer from two issues: (1) A large training set is required to construct a large visual vocabulary, making the construction computationally inefficient; (2) The generated visual vocabularies are heavily biased towards the training samples. In this work, we introduce a partitioned k-means clustering (PKM) scheme to efficiently generate a large and unbiased vocabulary using only a small training set. Instead of directly clustering training descriptors in their original space, we first split the original space into a set of subspaces and then perform a separate k-means clustering process in each subspace. Sequentially, we can build a complete visual vocabulary by combining different cluster centroids from multiple subspaces. Comprehensive experiments demonstrate that the proposed method indeed generates unbiased vocabularies and provides good scalability for building large vocabularies.
CITATION STYLE
Wei, S., Wu, X., & Xu, D. (2013). Partitioned k-means clustering for fast construction of unbiased visual vocabulary. In The Era of Interactive Media (Vol. 9781461435013, pp. 483–493). Springer New York. https://doi.org/10.1007/978-1-4614-3501-3_40
Mendeley helps you to discover research relevant for your work.