1. Low-resolution image categorization via heterogeneous domain adaptation.
- Author
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Yao, Yuan, Li, Xutao, Ye, Yunming, Liu, Feng, Ng, Michael K., Huang, Zhichao, and Zhang, Yu
- Subjects
- *
HETEROGENEOUS computing , *DISCRIMINATION (Sociology) , *DATA analysis , *ALGORITHMS , *IMAGE processing - Abstract
Abstract Most of existing image categorizations assume that the given datasets have a good resolution and quality. However, the assumption is often violated in real applications. In this paper, we study the low-resolution (LR) image categorization. By utilizing labeled high-resolution (HR) images as auxiliary information, we formulate the problem as a heterogeneous domain adaptation problem and propose a Discriminative Joint Distribution Adaptation (DJDA) model to solve it. The DJDA model projects both LR and HR images into an intermediate subspace with a well-designed objective function, where the distance between classes is expected to be enlarged and the distribution divergence to be reduced. As a result, the discriminative knowledge for HR images can be transferred effectively to LR images. Experimental results demonstrate the proposed DJDA method produces significantly superior categorization accuracies against state-of-the-art competitors. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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