Research on vocabulary sizes and codebook universality
Journal article, Peer reviewed
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Original versionLiu, W.-X., Hou, J., & Karimi, H. R. (2014). Research on vocabulary sizes and codebook universality. Abstract and Applied Analysis, 2014, 1-7. doi: 10.1155/2014/697245 10.1155/2014/697245
Codebook is an effective image representation method. By clustering in local image descriptors, a codebook is shown to be a distinctive image feature and widely applied in object classification. In almost all existing works on codebooks, the building of the visual vocabulary follows a basic routine, that is, extracting local image descriptors and clustering with a user-designated number of clusters. The problem with this routine lies in that building a codebook for each single dataset is not efficient. In order to deal with this problem, we investigate the influence of vocabulary sizes on classification performance and vocabulary universality with the kNN classifier. Experimental results indicate that, under the condition that the vocabulary size is large enough, the vocabularies built from different datasets are exchangeable and universal.
Published version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2014/697245 Open Access