Boundary and edge cues are highly beneficial in improving a wide variety of vision tasks
such as semantic segmentation, object recognition, stereo, and object proposal generation …

Subspace clustering aims to cluster unlabeled samples into multiple groups by implicitly
seeking a subspace to fit each group. Most of existing methods are based on a shallow …

Under the framework of graph-based learning, the key to robust subspace clustering and
subspace learning is to obtain a good similarity graph that eliminates the effects of errors …

Edge detection is among the most fundamental vision problems for its role in perceptual
grouping and its wide applications. Recent advances in representation learning have led to …

Under the framework of spectral clustering, the key of subspace clustering is building a
similarity graph, which describes the neighborhood relations among data points. Some …

In this paper, we address two challenging problems in unsupervised subspace learning: 1)
how to automatically identify the feature dimension of the learned subspace (ie, automatic …

In this paper, we recast the subspace clustering as a verification problem. Our idea comes
from an assumption that the distribution between a given sample x and cluster centers …

Edge detection plays an important role in image processing. With the development of deep
learning, the accuracy of edge detection has been greatly improved, and people have more …

Recently, we have proposed a novel physically-inspired method, called the Nearest Descent
(ND), which plays the role of organizing all the samples into an effective Graph, called the in …

Subspace learning aims to learn a projection matrix from a given training set so that a
transformation of raw data to a low-dimensional representation can be obtained. In practice …