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.
Original language | English |
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Pages (from-to) | 656-665 |
Number of pages | 10 |
Journal | Knowledge-Based Systems |
Volume | 163 |
DOIs | |
Publication status | Published - 1 Jan 2019 |
User-Defined Keywords
- Heterogeneous domain adaptation
- Low-resolution image categorization
- Subspace learning