摘要
由于多示例学习能够有效处理图像的歧义性,因此被应用于基于内容的图像检索(CBIR).本文提出一种基于多示例学习的 CBIR方法.该方法将图像作为多示例包,使用基于自组织特征映射网络聚类的方法分割图像,并将由颜色和纹理特征描述的图像区域作为包中示例.根据用户选择的实例图像生成正包和反包,使用多示例学习算法进行学习,实现图像检索和相关反馈.实验结果表明这种方法与已有方法检索效果相当,但检索效率更高.
Multi-instance learning has already been employed in Content-Based Image Retrieval （CBIR） for the reason that it is good at dealing with the inherent ambiguity of images. In this paper, a multi-instance learning based CBIR approach is presented. The whole image is regarded as a multi- instance bag. The image is partitioned into several regions using a Self-Organizing feature Map （SOM） clustering based image segmentation method , then the regions described by color and texture features are regarded as the instances in the bag. Next, query images posed by the user are transformed into corresponding positive and negative bags and a multi-instance algorithm is employed for image retrieval and relevance feedback . Experiments show that this approach achieves comparable results to some existing approaches and is even more efficient.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2006年第2期179-185,共7页
Pattern Recognition and Artificial Intelligence
基金
国家杰出青年科学基金(No.60325207)
国家自然科学基金(No.60473046)
全国优秀博士学位论文作者专项基金(No.200343)
973计划(No.2002CB312002)资助项目
