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Graph ranking is a promising technique for image retrieval, but its effectiveness is limited by the so-called semantic gap. To mitigate this gap, clickthroughs, which are helpful to perceive the visual content of images, are adopted by graph ranking models recently. However, few existing models take both sparseness and noisiness of clickthroughs into account, which are important in refining the clickthrough-based...
Graph-based ranking models, such as manifold ranking (MR), have been widely used in various image retrieval applications. To further improve such models, a current trend is to fuse the ranking results from multiple feature sets. Most of existing methods mainly concentrate on fusing the homogeneous feature sets derived from a single information channel, like the multiple modalities of image visual...
This paper presents a half-face dictionary integration (HFDI) algorithm for representation-based classification. The proposed HFDI algorithm measures residuals between an input signal and the reconstructed one, using both the original and the synthesized dual-column (row) half-face training samples. More specifically, we first generate a set of virtual half-face samples for the purpose of training...
Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are emerging as promising innovations for future network, which make Virtual Network Service (VNS) possible to be implemented broadly. It is the common truth that VNS is realized by the collaborations of multi-providers in practical scenario, where potential risks are lying in the collaborations. The primary risk is the availability...
We consider the problem of robust face recognition in which both the training and test samples might be corrupted because of disguise and occlusion. Performance of conventional subspace learning methods and recently proposed sparse representation based classification (SRC) might be degraded when corrupted training samples are provided. In addition, sparsity based approaches are time-consuming due...
With the rapid development of logistics industry, there are lots of logistics service and tons of data emerging on logistics platform. To match service and customers better, personalized recommendations logistics services is needed. In this paper, we introduce a collaborative filtering technology of e-commerce personalized recommendation system into the logistics platform. We present the application...
Self-reconfigurable robots is a micro-robot team with self-reconfiguration capabilities, strong flexibility and concealment and so on, it has broad application prospects in military reconnaissance, disaster relief, space operations and other occasions. in recent years, it becaming one of hotspots in the field of robotics research. But a single robot has poor perception capacity of environmental information...
Estimating agent's skill ratings from team competition results has many applications in the real world. Existing models assume skills are the same for all contexts. However, skills are context-sensitive in a variety of cases. In this paper, we present a Factor-Based Context-Sensitive Skill Rating System(FBCS-SRS). Instead of estimating agent skills under every context, which is hard due to data sparisity,...
This study applies fractal theory into the system integration of service enterprise, and proposes the concept named SOA-based service integration. Through analyzing the fractal characteristics of service integration, this article proves the applicability of fractal theory to the service integration based on three main reasons. Through the establishment of the layered model of service integration structure,...
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