基于结构化P2P的语义查询技术

由于P2P系统可以高效地对资源进行共享而受到关注,但现在的P2P仅支持精确查找或者通过洪泛方式进行低效率文本检索。为了解决这个问题,该文提出了一种结构化P2P环境中的文本检索系统,使用LSH函数将高维语义相关的文本向量映射相近的节点上,并解决了由此带来的负载均衡问题。实验结果显示该系统具有很好的查询准确率和负载均衡性能。     

基于局部敏感核稀疏表示的视频跟踪

为了解决l1范数约束下的稀疏表示判别信息不足的问题,该文提出基于局部敏感核稀疏表示的视频目标跟踪算法。为了提高目标的线性可分性,首先将候选目标的SIFT特征通过高斯核函数映射到高维核空间,然后在高维核空间中求解局部敏感约束下的核稀疏表示,将核稀疏表示经过多尺度最大值池化得到候选目标的表示,最后将候选目标的表示代入在线的SVMs,选择分类器得分最大的候选目标作为目标的跟踪位置。实验结果表明,由于利用了核稀疏表示下数据的局部性信息,使得算法的鲁棒性得到一定程度的提高。     

Multiple feature kernel hashing for large-scale visual search

Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods.     

面向视频语义分析的局部敏感的可鉴别稀疏表示

视频语义分析已经成为人们研究的热点。在传统稀疏表示方法中,相似视频特征未必能产生相近稀疏表示结果。在基于稀疏表示的视频语义分析中,假定相似的视频数据样本的稀疏表示也相似,即两个相似视频特征的稀疏系数之间的距离较小。为了提高视频语义分析的准确性,基于该假设提出一种面向视频语义分析的局部敏感的可鉴别稀疏表示方法。该方法在局部敏感稀疏表示中引入基于稀疏系数的鉴别损失函数,优化构建稀疏表示的字典,使稀疏表示特征满足类内离散度小、类间离散度大的Fisher准则,并建立可鉴别稀疏模型。为验证所提方法的有效性,在相关视频数据库中将其与多种算法进行对比,实验结果表明,该方法显著地提高了视频特征稀疏表示的鉴别性,有效地提高了视频语义分析的准确性。     

Locality-sensitive task allocation and load balancing in networked multiagent systems: Talent versus centrality

With the development of large scale multiagent systems, agents are always organized in network structures where each agent interacts only with its immediate neighbors in the network. Coordination among networked agents is a critical issue which mainly includes two aspects: task allocation and load balancing; in traditional approach, the resources of agents are crucial to their abilities to get tasks, which is called talent-based allocation. However, in networked multiagent systems, the tasks may spend so much communication costs among agents that are sensitive to the agent localities; thus this paper presents a novel idea for task allocation and load balancing in networked multiagent systems, which takes into account both the talents and centralities of agents. This paper first investigates the comparison between talent-based task allocation and centrality-based one; then, it explores the load balancing of such two approaches in task allocation. The experiment results show that the centrality-based method can reduce the communication costs for single task more effectively than the talent-based one, but the talent-based method can generally obtain better load balancing performance for parallel tasks than the centrality-based one.