Abstract and Applied Analysis

Research on Vocabulary Sizes and Codebook Universality

Wei-Xue Liu, Jian Hou, and Hamid Reza Karimi

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Abstract

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.

Article information

Source
Abstr. Appl. Anal., Volume 2014 (2014), Article ID 697245, 7 pages.

Dates
First available in Project Euclid: 2 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1412273199

Digital Object Identifier
doi:10.1155/2014/697245

Zentralblatt MATH identifier
07022901

Citation

Liu, Wei-Xue; Hou, Jian; Karimi, Hamid Reza. Research on Vocabulary Sizes and Codebook Universality. Abstr. Appl. Anal. 2014 (2014), Article ID 697245, 7 pages. doi:10.1155/2014/697245. https://projecteuclid.org/euclid.aaa/1412273199


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