Boosted Cross-Domain Categorization

Zhu, Fan, Shao, Ling and Tang, Jun (2014) Boosted Cross-Domain Categorization. In: 25th British Machine Vision Conference, BMVC 2014, 2014-09-01 - 2014-09-05.

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Abstract

A boosted cross-domain categorization framework that utilizes labeled data from other visual domains as the auxiliary knowledge for enhancing the original learning system is presented. The source domain data under a different data distribution are adapted to the target domain through both feature representation level and classification level adaptation. The proposed framework is working in conjunction with a learned domain adaptive dictionary pair, so that both the source domain data representations and their distribution are optimized in order to match the target domain. By iteratively updating the weak classifiers, the categorization system allocates more credits to “similar” source domain samples, while abandoning “dissimilar” source domain samples. Using a set of Web images and selected categories from the HMDB51 dataset as the source domain data, the proposed framework is evaluated with both image classification and human action recognition tasks on the Caltech-101 and the UCF YouTube datasets, respectively, achieving promising results.

Item Type: Conference or Workshop Item (Paper)
Faculty \ School: Faculty of Science > School of Computing Sciences
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Depositing User: Pure Connector
Date Deposited: 10 Feb 2017 02:29
Last Modified: 22 Apr 2020 09:37
URI: https://ueaeprints.uea.ac.uk/id/eprint/62416
DOI:

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