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Title:Cross-Categorization Transfer Learning Enhancing Image and Video Classification Performance
Author(s):Chang, Shiyu
Contributor(s):Huang, Thomas S.
Subject(s):machine learning
image classification
video classification
cross-category transfer learning
Abstract:In this paper, we concentrate on exploring the cross-category knowledge to enhance the information on the target categories with a small number of positive training examples. In many cases, even the intra-category knowledge may still be insufficient due to the scarce positive samples of the target category. On the other hand, transferring the cross-category knowledge is appealing as a way to solve or alleviate this problem by exploring knowledge in correlated categories. It is a quite challenging problem due to the nature of semantic differences among categories. To approach such cross-category transfer learning (CCTL), we propose to explore the intrinsic correlations between the source and target categories. A cross-category label propagation process is developed to transfer the category information from the source to the target categories. Moreover, the proposed CCTL can automatically detect when to transfer, which plays a role of “safety valve” to avoid the transfer of cross-category knowledge that is harmful for modeling the target category. The experiments in real-world image and video data sets demonstrate the competitive results and illustrate how the transfer processes connect between the source and target categories.
Issue Date:2011-05
Publication Status:unpublished
Peer Reviewed:not peer reviewed
Date Available in IDEALS:2014-01-15

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