Department of Electrical and Computer Engineering, University of British Columbia, Vancouver V6T 1Z4, Canada.
IEEE Trans Image Process. 2013 Aug;22(8):3108-19. doi: 10.1109/TIP.2013.2259836.
It is of great importance to investigate the domain adaptation problem of image object recognition, because now image data is available from a variety of source domains. To understand the changes in data distributions across domains, we study both the input and output kernel spaces for cross-domain learning situations, where most labeled training images are from a source domain and testing images are from a different target domain. To address the feature distribution change issue in the reproducing kernel Hilbert space induced by vector-valued functions, we propose a domain adaptive input-output kernel learning (DA-IOKL) algorithm, which simultaneously learns both the input and output kernels with a discriminative vector-valued decision function by reducing the data mismatch and minimizing the structural error. We also extend the proposed method to the cases of having multiple source domains. We examine two cross-domain object recognition benchmark data sets, and the proposed method consistently outperforms the state-of-the-art domain adaptation and multiple kernel learning methods.
研究图像目标识别的领域自适应问题非常重要，因为现在可以从各种源域获得图像数据。为了了解域间数据分布的变化，我们研究了跨域学习情况下的输入和输出核空间，其中大多数有标签的训练图像来自源域，而测试图像来自不同的目标域。为了解决由向量值函数引起的再生核希尔伯特空间中的特征分布变化问题，我们提出了一种域自适应输入-输出核学习（DA-IOKL）算法，该算法通过减少数据不匹配和最小化结构误差，同时利用判别向量值决策函数学习输入和输出核。我们还将提出的方法扩展到具有多个源域的情况。我们检验了两个跨域目标识别基准数据集，所提出的方法始终优于最新的域自适应和多内核学习方法。
