应用图学习算法的跨媒体相关模型图像语义标注  

Image semantic annotation of CMRM based on graph learning

作　　者：李玲[1] 宋莹玮 杨秀华[2] 陈逸杰 

机构地区：[1]吉林大学通信工程学院,吉林长春130012 [2]吉林大学网络中心,吉林长春130012

出　　处：《光学精密工程》2016年第1期229-235,共7页Optics and Precision Engineering

基　　金：国家自然科学基金资助项目(No.61371092);吉林省教育厅"十二五"科学技术研究项目(No.2014B006)

摘　　要：针对传统跨媒体相关模型(CMRM)只考虑图像的视觉信息与标注词之间的相关性,忽略标注词之间所具有的语义相关性的问题,本文提出了一种新的基于图学习算法的CMRM图像语义标注方法。该方法首先根据运动领域图片训练集中的标注词,建立运动领域本体来标注图像;然后采用传统的CMRM标注算法对训练集图像进行第一次标注,获得基于概率模型的图像标注结果;最后,根据本体概念相似度,利用图学习方法对第一次标注结果进行修正,在每幅图像的概率关系表中选择概率最大的N个关键词作为最终的标注结果,完成第二次标注。实验结果表明,本文提出的模型的查全率和查准率均高于传统的CMRM算法。The traditional Crossmedia Relevance Model（CMRM）is based on the relevance between visual information and annotation words, while ignoring the inter-word semantic relevance.Therefore,a new CMRM image semantic annotation model based on a graph learning was proposed.Firstly,the ontology of a sport field was established to label the images of the sport field according the annotation words in an image training set.Then,the traditional CMRM was adopted in the training images to complete the basic image annotations and obtain the image annotation result based on a probability model.Finally,the graph learning was used to refine the basic image annotations based on ontology concept similarity,and the top N keywords in the probability table for each image were chosen as the final annotation results.Experimental results show that the recall and precision of the proposed model are improved as compared with those of the traditional CMRMs.

关 键 词：图像分析 图像语义标注 跨媒体相关模型 本体 图学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]