Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization
Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization
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DOI：
10.1109/tip.2013.2290593
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发表时间：
2014-02-01
影响因子：
10.6
通讯作者：
Ma, Yi
中科院分区：
文献类型：
--
作者：
Gao, Shenghua;Tsang, Ivor Wai-Hung;Ma, Yi
通过为每个类别学习一个特定于类别的词典和为所有类别学习一个共享词典来实现细粒度的图像分类。这种特定类别的词典编码不同类别之间的细微视觉差异，而共享词典编码所有类别之间的共同视觉模式。为此，我们在不同词典之间施加不一致约束，以达到特征编码的目的。此外，为了使学习的词典稳定，我们还对每个词典施加了自不连贯的限制。我们提出的字典学习方法不仅适用于细粒度分类，而且改进了传统的基本级对象分类和其他任务，如事件识别。在五个数据集上的实验结果表明，该方法的性能优于最新的细粒度图像分类框架和基于稀疏编码的词典学习框架。所有这些结果都证明了我们方法的有效性。
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.