3D Patch-Based Sparse Learning for Style Feature Extraction

How to extract features of different shapes for style similarity evaluation is a very challenging research topic. Different from methods based on predefined style templates, we perform sparse learning and triplet embedding directly from the 3D shape local descriptors. The proposed method can adaptively generate element-level style features of different 3D patches for style similarity evaluation. The proposed algorithm mainly consists of the following steps. First, given a heterogeneous 3D shape collection, we learn mid-level style features by Siamese networks based on curvature-guided sampling directly on 3D shapes, which is a view-independent and part-independent approach. Second, we perform sparse coding to reduce the dimension of mid-level local features and achieve more compact and discriminative style features, named element-level features. Finally, we enhance our element-level style features by triplet embedding learning over crowdsourced triplets, which maps similar examples close to each other and dissimilar examples farther apart. The proposed algorithm can aggregate different local features into a global style feature representation and shows satisfactory discriminability. Our method can avoid the loss of style features from the 3D model to the views and can learn more discriminative style features by sparse learning. The experimental results show that our sparse style feature representation significantly improves the accuracy compared with recent state-of-the-art algorithms.