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.

中文翻译：

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学习弱风格分类的共识表示


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越来越受到关注。大多数现有方法侧重于从局部补丁或模式中提取判别特征。然而，风格分类中的分散现象尚未得到认识。这意味着风格类别中视觉上不太具有代表性的图像通常非常多样化并且很容易被错误分类。我们将它们命名为弱风格图像。使用多个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共识。也就是说，局部块中不同视觉特征的权重应该被分配相似的值。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），用于学习鲁棒的风格特征表示，特别是对于弱风格分类。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CSCAE，它在渐进集中过程中自适应地为不同特征分配权重。基于同一块的不同特征的权重应该相似的假设添加共识约束。具体来说，由“共享权重”思想和群体稀疏性推动的 CSCAE 线性对应物提高了功效和效率。为了进行评估，我们对时尚、漫画和建筑风格分类问题进行了广泛的实验。此外，我们还收集了一个新的数据集——在线购物，用于时尚风格分类，该数据集将公开用于基于视觉的时尚风格研究。 与最新的最先进的作品相比，实验证明了 SCAE 和 CSCAE 在公共数据集和新收集的数据集上的有效性。