Abstract
This chapter addresses two novel applications using deep learning: style recognition and kinship understanding. For the former, while style classification has drawn much attention in many fields such as fashion, architecture, and manga, most existing methods of style classification focus on extracting discriminative features from local patches or patterns. Usually multiple low-level visual features are extracted and concatenated together as the style descriptor. However, style classification usually relies on high-level abstract concepts. Meanwhile, there exists spread out phenomenon in style classification so that visually less representative images in a style class are usually very diverse and easily getting misclassified. In this section, we firstly describe related works and challenges in style classification task. Then we describe deep learning based solutions (i.e., consensus style centralizing autoencoder (CSCAE) [1]) addressing these challenges. We show experimental results on fashion, manga and architecture style classification problems with both deep learning and non-deep learning methods.
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