Subspace clustering is widely used to find clusters in different subspaces within a dataset. Autoencoders are popular deep subspace clustering methods using feature extraction and dimensional reduction. However, neural networks are vulnerable to overfitting, and therefore have limited potential for unsupervised subspace clustering. This paper proposes a deep multi-view subspace clustering network with feature boosting module to successfully extract meaningful features in different views and to fuse multi-view representations in a complementary manner for enhanced clustering results. The multi-view boosting provides the robust features for unsupervised clustering by emphasizing the features and removing the redundant noise. Quantitative and qualitative analysis on various benchmark datasets verifies that the proposed method outperforms state-of-the-art subspace clustering methods.

Photo style transfer aims to change the style of a given photo to a reference style image with the constraint by retaining the broad and faithful conservation of the content of the input image. Most previous algorithms still have challenging issues on how to exactly extract and represent the style of the image to avoid the interruption of human visual perception. In this paper, we present a texture preserving photo style transfer algorithm by separating the input image into texture and structure and then applying the deep structure style transfer network to effectively change the extracted style characteristics of the structure. The texture preserving photo style transfer overcomes the main drawback of the previous approaches like distortion and saturation of the boundary of the objects. The quantitative and qualitative experimental results including user study prove that the proposed photo style transfer is universally applicable comparing to remarkable previous approaches.

Robust vanishing point estimation has been widely applied to various applications in the field of computer vision and pattern recognition for robotics, advanced driver assistance systems, and autonomous driving vehicles. The major challenge for vanishing point detection lies in line segments, spurious vanishing candidate removal, and clustering for refinement. Recent vanishing point detection approaches have attempted to reduce the computational complexity involved with voting processes using optimized voter selection strategies to identify the vanishing point from line segments. This paper proposes a novel vanishing point detection method to select robust candidates, applying optimized minimum spanning tree-based clustering of the vanishing point candidates by analyzing the lines within a unit sphere domain. The proposed scheme was applied to an open database that included illumination, partial occlusion, and viewpoint changes to validate robustness without prior scene information.

The 3D object retrieval systems receive great concerns in the fields of pattern recognition and computer graphics because of their diverse applications. Traditional approaches of view-based 3D object retrieval have focused on finding descriptors which can efficiently represent the specific geometric information of the 3D object. By combining the local and the global features in order to improve the performance of 3D object retrieval, we propose a sparse coding based feature optimization technique using the hybrid gradient features of the projected images from 3D object. Experimental results show the effectiveness of our proposed approach.