This paper presents a novel method to determine the discriminative image representation via visual dictionary learning framework for image classification task. Visual dictionary learning has the capacity to represent input image using an over-complete element set. Sparsity restrains distractors and prevents over-fitting. The two main characteristics benefit the classification solution. However, one shortcoming of existing dictionary learning is that it neglects to exploit the potential correlations across visual elements, especially from the category-specific feature space. To address this problem, we first propose to learn multiple discriminative category-specific dictionaries (DCSD) from all categories. The DCSD can explore the visual elements from each category in terms of sharable property. For this reason, these learned category-specific visual elements encourage image features from the same class to have the similar feature representations. In addition, exemplary data reflect the main characteristic of whole dataset and can improve the performance of algorithm that employs them. Therefore, we further propose a representative pattern dictionary (RPD) model to discover the exemplary visual elements for promoting the discriminative capability of feature representation. These exemplary visual elements are essentially a subset of over-complete visual elements and can represent the whole sample data effectively. Finally, we design a novel strategy that integrates the merits of object proposals and deep features jointly to strengthen the semantic information of image-level feature. Experimental results on benchmark datasets demonstrate the effectiveness of our method, which is shown to be superior to the recently competing dictionary learning and deep learning based image classification approaches.

中文翻译：

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学习用于图像分类的特定类别的可共享和示例性视觉元素


本文提出了一种通过图像分类任务的视觉字典学习框架来确定判别性图像表示的新方法。视觉字典学习能够使用超完备元素集来表示输入图像。稀疏性可以抑制干扰因素并防止过度拟合。这两个主要特征有利于分类解决方案。然而，现有词典学习的一个缺点是它忽略了利用视觉元素之间的潜在相关性，特别是来自特定类别特征空间的相关性。为了解决这个问题，我们首先建议从所有类别中学习多个判别性类别特定词典（DCSD）。 DCSD 可以在可共享财产方面探索每个类别的视觉元素。因此，这些学习到的特定于类别的视觉元素鼓励来自同一类别的图像特征具有相似的特征表示。此外，示例数据反映了整个数据集的主要特征，可以提高使用它们的算法的性能。因此，我们进一步提出了一种代表性模式字典（RPD）模型来发现示例性视觉元素，以提高特征表示的辨别能力。这些示例性视觉元素本质上是超完备视觉元素的子集，并且可以有效地表示整个样本数据。最后，我们设计了一种新颖的策略，将对象提案和深层特征的优点结合起来，以增强图像级特征的语义信息。 基准数据集上的实验结果证明了我们方法的有效性，该方法优于最近竞争的基于字典学习和深度学习的图像分类方法。