Learning Consensus Representation for Weak Style Classification
Learning Consensus Representation for Weak Style Classification
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DOI：
10.1109/tpami.2017.2771766
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发表时间：
2018-12-01
影响因子：
23.6
通讯作者：
Fu, Yun
中科院分区：
文献类型：
--
作者：
Jiang, Shuhui;Shao, Ming;Fu, Yun
风格分类（例如巴洛克和哥特式建筑风格）在时尚、建筑和漫画等许多领域受到越来越多的关注。现有的大多数方法侧重于从局部区域或模式中提取具有判别性的特征。然而，风格分类中的离散现象尚未得到认识。这意味着一个风格类别中视觉上不太具代表性的图像通常非常多样，并且很容易被误分类。我们将它们称为弱风格图像。在使用多种视觉特征进行有效的弱风格分类时的另一个问题是不同特征之间缺乏一致性。也就是说，局部区域中不同视觉特征的权重本应被赋予相似的值。为了解决这些问题，我们提出了一种共识风格集中自动编码器（CSCAE），用于学习稳健的风格特征表示，特别是针对弱风格分类。首先，我们提出了一种风格集中自动编码器（SCAE），它以渐进的方式集中弱风格特征。然后，基于SCAE，我们提出了非线性和线性版本的CSCAE，它们在渐进集中过程中自适应地为不同特征分配权重。基于同一区域不同特征的权重应该相似的假设添加了共识约束。具体而言，受“共享权重”思想以及组稀疏性启发的所提出的CSCAE的线性对应物提高了有效性和效率。为了进行评估，我们在时尚、漫画和建筑风格分类问题上进行了广泛的实验。此外，我们收集了一个新的数据集——用于时尚风格分类的在线购物数据集，它将公开用于基于视觉的时尚风格研究。实验表明，与最新的前沿研究成果相比，SCAE和CSCAE在公共数据集和新收集的数据集上均有效。
Style classification (e.g., Baroque and Gothic architecture style) is grabbing increasing attention in many fields such as fashion, architecture, and manga. Most existing methods focus on extracting discriminative features from local patches or patterns. However, the spread out phenomenon in style classification has not been recognized yet. It means that visually less representative images in a style class are usually very diverse and easily getting misclassified. We name them weak style images. Another issue when employing multiple visual features towards effective weak style classification is lack of consensus among different features. That is, weights for different visual features in the local patch should have been allocated similar values. To address these issues, we propose a Consensus Style Centralizing Auto-Encoder (CSCAE) for learning robust style features representation, especially for weak style classification. First, we propose a Style Centralizing Auto-Encoder (SCAE) which centralizes weak style features in a progressive way. Then, based on SCAE, we propose both the non-linear and linear version CSCAE which adaptively allocate weights for different features during the progressive centralization process. Consensus constraints are added based on the assumption that the weights of different features of the same patch should be similar. Specifically, the proposed linear counterpart of CSCAE motivated by the "shared weights" idea as well as group sparsity improves both efficacy and efficiency. For evaluations, we experiment extensively on fashion, manga and architecture style classification problems. In addition, we collect a new dataset-Online Shopping, for fashion style classification, which will be publicly available for vision based fashion style research. Experiments demonstrate the effectiveness of the SCAE and CSCAE on both public and newly collected datasets when compared with the most recent state-of-the-art works.