Multi-manifold metric learning for face recognition based on image sets

In this paper, we propose a new multi-manifold metric learning (MMML) method for the task of face recognition based on image sets. Different from most existing metric learning algorithms that learn the distance metric for measuring single images, our method aims to learn distance metrics to measure the similarity between manifold pairs. In our method, each image set is modeled as a manifold and then multiple distance metrics among different manifolds are learned. With these distance metrics, the intra-class manifold variations are minimized and inter-class manifold variations are maximized simultaneously. For each person, we learn a distance metric by using such a criterion that all the learned distance metrics are person-specific and thus more discriminative. Our method is extensively evaluated on three widely studied face databases, i.e., Honda/UCSD database, CMU MoBo database and YouTube Celebrities database, and compared to the state-of-the-arts. Experimental results are presented to show the effectiveness of the proposed method.     

Combining the requirement information for software defect estimation in design time

This paper analyzes the ability of requirement metrics for software defect prediction. Statistical significance tests are used to compare six machine learning algorithms on the requirement metrics, design metrics, and combination of both metrics in our analysis. The experimental results show the effectiveness of the predictor built on the combination of the requirement and design metrics in the early phase of the software development process.     

On Averaging Rotations

In this paper two common approaches to averaging rotations are compared to a more advanced approach based on a Riemannian metric. Very often the barycenter of the quaternions or matrices that represent the rotations are used as an estimate of the mean. These methods neglect that rotations belong to a non-linear manifold and re-normalization or orthogonalization must be applied to obtain proper rotations. These latter steps have been viewed as ad hoc corrections for the errors introduced by assuming a vector space. The article shows that the two approximative methods can be derived from natural approximations to the Riemannian metric, and that the subsequent corrections are inherent in the least squares estimation.     

A decision framework for the analysis of green supply chain contracts: An evolutionary game approach

The overwhelming growth in the struggle between economy and ecology has led managerial research to promote methodologies to reap profits ensuring sustainability of the ecosystem thus venturing into the novel domain incorporating sustainability with supply chain management and coordination. In this paper, we look for a synergetic alliance between the environmental and commercial benefits by establishing coordination between the producer and the retailer to adjudicate their strategies to trigger green practices with the focus on maximizing economic profits by leveraging upon the product’s greenness. A two player game has been developed to stand synonymous to the situation considered. Special considerations have been devised to augment the analysis with the consumer psychology towards the green products to establish a practical outlook involving the greenness and economic returns. Evolutionary dynamics has been adopted extensively to locate the optimal and the most stable point offering the best economic gains. The economically suitable solutions are then substantiated on an existing sustainability metric and hence we aim to offer a holistic view into supply chain dynamics from the perspective of environmental management. The insights drawn provide managerial implications and advantages of having environment as a key focus along with business motives.     

STRUCTURE OF POSSIBILISTIC INFORMATION METRICS AND DISTANCES: PROPERTIES

Systematic analysis of identities and inequalities satisfied by possibilistic information distances is conducted. The analysis is based on their representations as discrete sums and on certain inequalities for rearrangements of sequences. These identities and inequalities express several properties that are usually deemed characteristic of information distances and measures. In the companion paper those properties are used to obtain several axiomatic characterizations of possibility distances. The basic distance is g(p,q) defined for the distributions p = (Pi,[tdot],pn) and q = (qi,[tdot],qn) such that Pi≤qi. =i=1,[tdot],n They serve to define a metric G(p,q)=g<p,p ∨ q)+g(q,p [tdot] q) and a distanceH(p,q) = g(p∧q,p)+g(p ∧ q,q). All these distances are, in turn based on the U-uncertainty information function.     

基于加权重叠率的单目标视觉跟踪评价指标

针对真值标注的歧义性、偏差性问题和具有缩放场景的视觉跟踪应用情况,提出了一种新的视觉跟踪单目标基准评价指标.首先,在重叠率基础上提出了加权重叠率框架;其次,提出了多区域标注方法,通过多区域标注降低标注者歧义性,在具有缩放场景的应用中,通过反演进行多区域标注,使评价更符合应用实际;再次,针对标注的偏差性,提出了多标注融合方法,提高了标注的可信度;最后,将应用于单次跟踪评价的重叠率框架推广到多次跟踪评价,利用加权结果图使评价更具解释性.通过著名评价标准VOT、OTB的真值标注融合实验验证了本文标注规则的准确性;通过在具有缩放场景的视觉跟踪实验和重复实验,与其他跟踪指标的比较验证了本文指标的有效性.     

Set‐theoretic adaptive filtering based on data‐driven sparsification

In this article, we propose a fast and efficient algorithm named the adaptive parallel Krylov‐metric projection algorithm. The proposed algorithm is derived from the variable‐metric adaptive projected subgradient method, which has recently been presented as a unified analytic tool for various adaptive filtering algorithms. The proposed algorithm features parallel projection—in a variable‐metric sense—onto multiple closed convex sets containing the optimal filter with high probability. The metric is designed based on (i) sparsification by means of a certain data‐dependent Krylov subspace and (ii) maximal use of the obtained sparse structure for fast convergence. The numerical examples show the advantages of the proposed algorithm over the existing ones in stationary/nonstationary environments. Copyright © 2011 John Wiley & Sons, Ltd.     

Facial expression cloning with elastic and muscle models

Expression cloning plays an important role in facial expression synthesis. In this paper, a novel algorithm is proposed for facial expression cloning. The proposed algorithm first introduces a new elastic model to balance the global and local warping effects, such that the impacts from facial feature diversity among people can be minimized, and thus more effective geometric warping results can be achieved. Furthermore, a muscle-distribution-based (MD) model is proposed, which utilizes the muscle distribution of the human face and results in more accurate facial illumination details. In addition, we also propose a new distance-based metric to automatically select the optimal parameters such that the global and local warping effects in the elastic model can be suitably balanced. Experimental results show that our proposed algorithm outperforms the existing methods.     

电力计量标准化管理探讨

计量管理是一项系统工程,各个环节都要有标准,不仅有技术标准,而且还要有管理标准,工作标准等,因此需要建立一套完整的标准化体系,才能深入实施计量的标准化管理工作。