IEEE Trans Image Process. 2014 Feb;23(2):623-34. doi: 10.1109/TIP.2013.2290593. Epub 2013 Nov 12.
This paper targets fine-grained image categorization by learning a category-specific dictionary for each category and a shared dictionary for all the categories. Such category-specific dictionaries encode subtle visual differences among different categories, while the shared dictionary encodes common visual patterns among all the categories. To this end, we impose incoherence constraints among the different dictionaries in the objective of feature coding. In addition, to make the learnt dictionary stable, we also impose the constraint that each dictionary should be self-incoherent. Our proposed dictionary learning formulation not only applies to fine-grained classification, but also improves conventional basic-level object categorization and other tasks such as event recognition. Experimental results on five data sets show that our method can outperform the state-of-the-art fine-grained image categorization frameworks as well as sparse coding based dictionary learning frameworks. All these results demonstrate the effectiveness of our method.
本文旨在通过为每个类别学习一个特定于类别的字典和一个所有类别的共享字典来实现细粒度图像分类。这些特定于类别的字典编码了不同类别之间的细微视觉差异，而共享字典则编码了所有类别之间的常见视觉模式。为此，我们在特征编码的目标中对不同字典施加不连贯性约束。此外，为了使学习到的字典稳定，我们还施加了每个字典应该自不相关的约束。我们提出的字典学习公式不仅适用于细粒度分类，而且还改进了传统的基本级别对象分类以及其他任务，例如事件识别。在五个数据集上的实验结果表明，我们的方法可以优于最先进的细粒度图像分类框架以及基于稀疏编码的字典学习框架。所有这些结果都证明了我们方法的有效性。
