快速图像标注的改进跨媒体相关模型

Improved cross-media relevance model for quick image annotation

针对跨媒体相关模型(CMRM)标注效率低、标注效果差的不足,提出了改进的跨媒体相关模型。提出的模型在改进了词汇平滑处理方法的基础之上,通过简洁的图像特征表示方法和相似度计算方法更准确地度量了图像与图像之间的相关性。在Corel5k数据集上的实验结果表明,所提出的改进CMRM标注效率显著提高,性能是原始CMRM的近3倍,而且,也优于高质量的标注模型,如著名的多伯努利相关模型(MBRM)和有指导的多类标签(SML)等模型。

To overcome the shortcomings of Cross-Media Relevance Model （CMRM） whose efficiency and effectiveness are low, an improved CMRM was proposed. Based on the improved smoothing method for textual words, the improved CMRM simplified the feature representation and similarity computation which made the measure of relationship between image and image more accurate. The experimental results on the CorelSk dataset show that the proposed approach can significantly improve annotation efficiency. The performance of the improved CMRM is almost three times as good （ in terms of mean F1- measure） as original CMRM, also, better than some previously published high quality algorithms such as famous Multiple Bernoulli Relevance Model （MBRM） and Supervised Muhiclass Labeling （SML）.