一种融合图学习与区域显著性分析的图像检索算法

Combining Graph Learning and Region Saliency Analysis for Content-Based Image Retrieval

为弥合图像低层视觉特征和高层语义之间的语义鸿沟,改善图像检索的效果,机器学习算法经常被引入到图像检索问题中.通常情况下,机器学习算法是与相关反馈机制相结合,通过用户的交互操作,标定出若干正反例图像,很自然地就可以将图像检索问题转化为模式识别中的分类问题.目前融合区域显著性分析的区域图像检索算法尚没有与机器学习算法相融合.本文结合图像区域显著性分析,并针对用户参与反馈的情况,分别提出了两种图像检索解决方案.其一,在没有用户反馈以及用户只反馈正例图像的情形下,将图像检索问题转化为直推式学习问题(Transductive Learning),改进已有的基于图的半监督学习算法,提出了融合区域显著性分析的层次化图表示(Hierarchi-cal Graph Representation)方式,用以实现标记传播;其二,在用户同时反馈正反例图像的情形下,利用用户反馈得到的正反例图像构建相似性邻接矩阵,通过流形排序算法(Manifold-Ranking)学习出用户感兴趣的查询目标概念并用相应的特征向量集合表示,并据此查询图像库返回用户语义相关的图像集合.实验结果验证了这两种检索策略的有效性.

For the image retrieval task which combines machine learning theory with relevance feedback mechanism,this paper focuses on the graph-based semisupervised learning algorithm with application to region-based image retrieval.Different schemes which both incorporate the region saliency into the graph-based semi-supervised learning framework are applied to deal with two types of feedback.Firstly,in the case that no sample or only positive samples are available from the user＇s feedback,the retrieval task can be resolved via a transductive learning manner,a hierarchical graph model which incorporates region saliency information is constructed and the manifold-ranking algorithm is adopted subsequently for positive label propagation.Secondly,in the case that the user provides both positive and negative samples,the region-level adjacency matrix will be constructed via the feedback samples,and the manifold-ranking algorithm is also adopted here to choose instances which truly represent the user＇s query semantics.The selected instances are then used to retrieve the relevant samples.The experiments have proved the effectiveness of the proposed method.