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.

Text-based retrieval systems have been popular, but content-based retrieval systems have gained widespread acceptance in recent years to directly retrieve diverse media based on their visual content, such as color, texture, and shape. Among many content-based retrieval systems, sketch-based media retrieval systems have attracted attention recently with the proliferation of tablet PCs and smart mobile devices. Sketch-based retrieval requires the user to draw a freehand sketch query, but freehand drawing can be challenging for those with limited drawing skills. This degrades retrieval performance, since successful retrieval depends on the quality of the sketch query image drawn by the user. To address this issue, we propose a real-time stroke guidance for freehand sketch retrieval that continuously displays next-stroke shadow sketches on the canvas based on the user's step-by-step partial strokes. We train a stroke guidance network that learns the mapping between the step-wise stroke relations to predict the user's next stroke. The proposed stroke guidance for freehand sketch retrieval system runs on a five step next-stroke prediction model that identifies candidate next-stroke sketches from a database of millions of sketches. The system retrieves variable number of sketch object classes at different drawing stages. During the initial sketching stage, diverse drawing possibilities are covered by retrieving multiple sketch classes; as the sketching progresses, the intended sketch class is narrowed down to one. Deep binary hashing is employed for efficient similarity matching of relevant next-stroke sketches. We extend the Google QuickDraw dataset to create a five step sketch stroke database. Qualitative and quantitative experiments are conducted to verify the effectiveness of the proposed system, which can be utilized for drawing guidance, tracing, and sketch retrieval. Tracing refers to the act of copying the shadowed line of a guiding image by drawing over its lines.

Structure-preserving image decomposition separates a given image into structure and texture by smoothing the image, simultaneously preserving or enhancing image edges. The well-studied problem of image decomposition is applied to various areas, such as image smoothing, detail enhancement, non-photorealistic rendering, image artistic rendering, and high-dynamic-range compression. In this paper, we propose a fast algorithm for structure-preserving image decomposition that adopts total variation (TV) minimization to the moving least squares (MLS) method with non-local weights, called structure adaptive TV (SATV) minimization. MLS with non-local weights provides high accuracy approximation that is robust to noise, and allows a fast convergence with TV regularization term. As a result, our proposed SATV preserves the dominant structure while flattening fine-scale details. The experimental results show that the SATV minimization algorithm provides faster and more robust image decomposition than the well-known previous approaches. We demonstrate the usefulness of our algorithm by presenting successful applications in image smoothing and detail enhancement.