摘要
传统基于超图的图像分类方法在构建超图时并未考虑各超边之间的关系,导致最终分类效果不理想.文中结合图像视觉信息和标注信息量化超边间相关性,提出一种基于超边相关性的图像分类方法,有效地将图像相关的标注信息作为判定图像类别的指标引入到图像分类中,进而对图像进行更准确的分类.在LabelMe和UIUC数据集上的实验验证该方法的有效性.
Traditional hypergraph-based image classification methods overlook the correlation among hyperedges in hypergraph construction, which results in poor classification performance. A method based on hyperedge correlation is proposed in this paper. The correlation among hyperedses is quantified by combining the image vision and its corresponding tags information. The tags corresponding to the image are introduced into the image classification as indicator information and thus better classification performance is obtained. The effectiveness of the proposed method is verified by experiments conducted on datasets such as LabelMe and UIUC.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2014年第2期120-126,共7页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金(No.61033013
61370129
61375062)
北京市自然科学基金(No.4112046)
教育部博士点基金(No.20120009110006)
中央高校基本科研业务费专项基金项目资助
关键词
图像分类
超图学习
语义融合
Image Classification
Hypergraph Learning
Semantic Fusion
