Real world data is usually high dimensional, and dimensionality reduction can significantly improve the efficiency of
data processing and analysis. Existing approaches relying on distances between neighboring features typically suffer
from the unreliable estimation of the true distance on a feature manifold due to its non-convexity. An approach is
proposed to solve the problem by discarding long geodesics poisoned by boundary points indiscriminately. However,
despite the improved performance, there are two major shortcomings with the approach. First, many long geodesics
poisoned by few boundary points, which contribute little to the distortion of a manifold, are thrown away, as may
decrease the robustness without improving the distortion of the manifold. Second, since short geodesics are sensitive to
noise, retaining the whole effect of them may result in the bad robustness. This paper presents a regularization
framework for nonlinear dimensionality reduction that incorporates long geodesics poisoned by few boundary points and
reduces the effect of short geodesics, to realize isometry largely. In addition, the approach is sensitive to non-uniform
sampling. To cope with the issue, we describe an improved robust boundary detection method. Experimental results are
presented to illustrate the better performance of the proposed algorithm on two standard data sets.
We propose a moving objects segmentation method for color image sequences based on the piecewise constant Mumford-Shah model (also known as the C-V model) solving by the semi-implicit additive operator splitting (AOS) scheme, which is unconditionally stable, fast, and easy to implement. The method first uses the Gaussian mixture model for background modeling and then subtracts the background to obtain the moving regions that are the handling objects of our method. As a result of the introduction of the AOS scheme, we could use a rather large time step and still maintain the stability of the evolution process. Additionally, the method can easily be parallelized because the AOS scheme decomposes the equations into a sequence of one-dimensional (1-D) systems. The experimental results demonstrate that under real moving objects video tests, the AOS scheme accelerates the evolution of the curve and significantly reduces the number of iterations, and also demonstrates the validity of our method.
Image compression is one of important processing techniques in visual salient object detecting. This paper proposes a
model of image compression including visual attention and encoding of image. For a given image, salient regions are
detected by visual attention; salient sizes are acquired by calculating feature difference between known location and
periphery regions. In order to achieving variable resolution image compression, various compression ratios are adopted
according to the saliency order from high to low. Original resolution is retained in the first salient region; the lowest
resolution is applied in the inapparent salient regions and the middle resolution is decided by the saliency order from
high to low. By this method, we achieve variable resolution image compression by the model of visual attention.
Experimental results indicate that the model of image compression not only can achieve high compression ratio in total
image but also can keep high resolution in salient regions and the quality of compressed image decreases as compression
ratio increases. This method presents a new model for image compression.