Editorial  

作　　者：XU HaiJiao HUANG ChangQin PAN Peng ZHAO GanSen XU ChunYan LU YanSheng CHEN Deng WU JiYi 

机构地区：[1]School of Computer Science and Technology, Huazhong University of Science and Technology [2]Research Center for Information Services and Software Technology, South China Normal University [3]E-Service Research Center, Zhejiang University [4]Electrical and Computer Engineering, National University of Singapore

出　　处：《Science China(Physics,Mechanics & Astronomy)》2015年第12期1-2,共2页中国科学：物理学、力学、天文学（英文版）

摘　　要：This issue of Science China Physics, Mechanics ＆ Astronomy celebrates the Centenary of Einstein＇s General Theory of Rela- tivity, which changed the way humanity understood the concepts of space, time and matter. Prior to 1915 Einstein had intro- duced his theory of Special Relativity, and Minkowski had introduced the spacetime metric. General Relativity overthrew the Newtonian idea that space, time and matter were independent, replacing it with the idea that space, time and matter are inex- tricably linked. Within a year of the publication of General Relativity came Schwartzchild＇s exact solution of Einstein＇s field equations which describes the spacetime structure of black holes. In 1916 and 1918 Einstein showed that his theory predicted the existence of gravitational waves. Within 7 years, in 1922, Friedmann published a solution for Einstein＇s field equations applied to a homogeneous universe, uncovering the basic physics of Big Bang cosmology.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.

关 键 词：multi-concept image retrieval semantic correlation probability estimation concept learning visual evidence 

分 类 号：O412.1[理学—理论物理] P159.3[理学—物理]