NDLI: Combining different visual vocabularies with different sizes for image categorization

Content Provider	IEEE Xplore Digital Library
Author	Hui-Lan Luo Hui Wei Yuan Ren
Copyright Year	2009
Description	Author affiliation: Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, 200433, China (Hui-Lan Luo; Hui Wei; Yuan Ren)
Abstract	In this paper, the advantages of ensemble methods are applied to image categorization. A novel method is introduced for image categorization by combining various visual vocabularies with different sizes in the popular vocabulary approach. The vocabulary approach describes an image as a bag of discrete visual codewords, where the frequency distributions of these words are used for image categorization. Based on vocabularies of various sizes, a classifier ensemble is learned, which can jointly exploit different information with various granularities. High classification accuracies of the proposed algorithm are demonstrated on four different datasets.
Starting Page	258
Ending Page	261
File Size	2115387
Page Count	4
File Format	PDF
ISBN	9781424446063
DOI	10.1109/PACIIA.2009.5406651
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vocabulary Dictionaries Laboratories Humans object categorization ensemble learning Histograms visual codebook Information processing Computer industry Computational intelligence Testing Image classification
Content Type	Text
Resource Type	Article

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