With the rapid development of future network, there has been an explosive growth in multimedia data such as web images. Hence, an efficient image retrieval engine is necessary. Previous studies concentrate on the single concept image retrieval, which has limited practical usability. In practice, users always employ an Internet image retrieval system with multi-concept queries, but, the related existing approaches are often ineffective because the only combination of single-concept query techniques is adopted. At present semantic concept based multi-concept image retrieval is becoming an urgent issue to be solved. In this paper, a novel Multi-Concept image Retrieval Model(MCRM) based on the multi-concept detector is proposed, which takes a multi-concept as a whole and directly learns each multi-concept from the rearranged multi-concept training set. After the corresponding retrieval algorithm is presented, and the log-likelihood function of predictions is maximized by the gradient descent approach. Besides, semantic correlations among single-concepts and multiconcepts are employed to improve the retrieval performance, in which the semantic correlation probability is estimated with three correlation measures, and the visual evidence is expressed by Bayes theorem, estimated by Support Vector Machine(SVM). Experimental results on Corel and IAPR data sets show that the approach outperforms the state-of-the-arts. Furthermore, the model is beneficial for multi-concept retrieval and difficult retrieval with few relevant images.
复杂网络的主题社区挖掘具有重要的应用价值,但现有方法可扩展性差,无法高效挖掘大规模复杂网络的主题社区.针对该问题,提出一种基于分布式非负矩阵分解的主题社区挖掘方法:TCMDNMF(topic community mining based on distributed nonnegative matrix factorization),该方法基于非负矩阵联合分解模型,可以有效统一集成节点链接和内容信息挖掘主题社区.通过采用梯度下降方法对主题社区挖掘模型进行了优化求解,并引入L1范数作为稀疏性正则项以及基于Map Reduce分布式计算框架提高了关键算法的计算效率.实验结果表明,TCMDNMF不仅可以有效挖掘主题社区,而且具有高度可扩展性,可以有效解决大规模复杂网络主题社区挖掘带来的大数据量计算问题.
