作者：
Sekita, IKurita, TOtsu, NAbdelmalek, NNElectrotechnical Laboratory
Tsukuba Japan 305 Ottawa
Ontario Canada Nabih Nessim Abdelmalek:received his B.S. degree in Electrical Engineering in 1951 and his B.S. degree in Mathematics in 1954
both from Cairo University Egypt and his Ph.D. degree in Theoretical Nuclear Physics in 1958 from Manchester University U.K. After 7 years in the Department of Mathematics on the Faculty of Engineering Cairo University he moved to Canada and became a member of the scientific staff at Bell Northern Research in Ottawa. After a period of 2 years he joined the National Research Council of Canada in Ottawa. His current interest is the application of numerical analysis techniques to the problem of image processing particularly image enhancement and restoration data compression pattern classification parameter estimation and 3D robot vision.
Thresholding techniques are fundamental for region segmentation of a gray level image. It is often realistic to assume that gray pixel levels are subject to a mixture of normal distributions. This paper points out pro...
详细信息
Thresholding techniques are fundamental for region segmentation of a gray level image. It is often realistic to assume that gray pixel levels are subject to a mixture of normal distributions. This paper points out problems caused by the underestimation of variances from a frequency distribution and solves the problems by transforming a frequency distribution into a piecewise-continuous distribution based on the maximum entropy criterion. This transformation improves conventional thresholding techniques. Experimental results showed the effectiveness of the transformation.
The Anchor-based Multi-view Subspace Clustering (AMSC) has turned into a favourable tool for large-scale multi-view clustering. However, there still exist some limitations to the current AMSC approaches. First, they t...
详细信息
The Anchor-based Multi-view Subspace Clustering (AMSC) has turned into a favourable tool for large-scale multi-view clustering. However, there still exist some limitations to the current AMSC approaches. First, they typically recover anchor graph structure in the original linear space, restricting their feasibility for nonlinear scenarios. Second, they usually overlook the potential benefits of jointly capturing the inter-view and intra-view information for enhancing the anchor representation learning. Third, these approaches mostly perform anchor-based subspace learning by a specific matrix norm, neglecting the latent high-order correlation across different views. To overcome these limitations, this paper presents an efficient and effective approach termed Large-scale Tensorized Multi-view Kernel Subspace Clustering (LTKMSC). Different from the existing AMSC approaches, our LTKMSC approach exploits both inter-view and intra-view awareness for anchor-based representation building. Concretely, the low-rank tensor learning is leveraged to capture the high-order correlation (i.e., the inter-view complementary information) among distinct views, upon which the \(l_{1,2}\) norm is imposed to explore the intra-view anchor graph structure in each view. Moreover, the kernel learning technique is leveraged to explore the nonlinear anchor-sample relationships embedded in multiple views. With the unified objective function formulated, an efficient optimization algorithm that enjoys low computational complexity is further designed. Extensive experiments on a variety of multi-view datasets have confirmed the efficiency and effectiveness of our approach when compared with the other competitive approaches.
暂无评论