Style classification (e.g., architectural, music, fashion) attracts an increasing attention in both research and industrial fields. Most existing works focused on low-level visual features composition for style representation. However, little effort has been devoted to automatic mid-level or high-level style features learning by reorganizing low-level descriptors. Moreover, styles are usually spread out and not easy to differentiate from one to another. In this paper, we call these less representative images as weak style images. To address these issues, we propose a consensus style centralizing auto-encoder (CSCAE) to extract robust style features to facilitate weak style classification. CSCAE is the ensemble of several style centralizing auto-encoders (SCAEs) with consensus constraint. Each SCAE centralizes each feature of certain category in a progressive way. We apply our method in fashion style classification and manga style classification as two example applications. In addition, we collect a new dataset, Online Shopping, for fashion style classification evaluation, which will be publicly available for vision based fashion style research. Experiments demonstrate the effectiveness of SCAE and CSCAE on both public and newly collected datasets when compared with the most recent state-of-the-art works.

Tensor completion is an important topic in the area of image processing and computer vision research, which is generally built on extraction of the intrinsic structure of the tensor data. Drawing on this fact, action classification, relying heavily on the extracted features of high-dimensional tensors, may indeed benefit from tensor completion techniques. In this paper, we propose a low-rank tensor completion method for action classification, as well as image recovery. Since there may exist distortion and corruption in the tensor representations of video sequences, we project the tensors into a subspace, which contains the invariant structure of the tensors. In order to integrate useful supervisory information for classification, we adopt a discriminant analysis criterion to learn the projection matrices. The resulting multi-variate optimization problem can be effectively solved using the augmented Lagrange multiplier (ALM) algorithm. Experiments demonstrate that our method results with better accuracy compared with some other state-of-the-art low-rank tensor representation learning approaches on the MSR Hand Gesture 3D database and the MSR Action 3D database. By denoising the Multi-PIE face database, our experimental setup testifies the proposed method can also be employed to recover images.