摘要
在基于内容的图像检索中,建立图像底层视觉特征与高层语义的联系是个难题.一个新的解决方法是按照图像的语义内容进行自动标注.为了缩小语义差距,采用基于支持向量机(SVM)的多类分类器为空间映射方法,将图像的底层特征映射为具有一定高层语义的模型特征以实现概念索引,使用的模型特征为多类分类的结果以概率形式组合而成.在模型特征组成的空间中,再使用核函数方法对关键词进行了概率估计,从而提供概念化的图像标注以用于检索.实验表明,与底层特征相比,使用模型特征进行自动标注的结果F度量相对提高14%.
Automatic image annotation is an important but highly challenging problem in content-based image retrieval. A new procedure for providing images with semantic keywords is introduced. To over the semantic gap, classified images are used to train a special multi-class classifier based on support vector machine （SVM）, which maps the visual image feature into the model space to achieve the concept indexing. The model-vectors that construct the model space are the combination of the multi-class classifier＇s outputs, and applied to each individual image. Soft labels are then given to the unannotated images during the propagation procedure in the model space, and as keyword, each label is associated with a membership confidence estimated by a biased kernel regression algorithm. Thus conceptualized annotations of images could be provided to users. The empirical study on the COREL image database shows that the proposed model-vectors outperform visual features 14.0 % in F-measure for annotation comparatively.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2007年第3期452-459,共8页
Journal of Computer Research and Development
基金
国家"九七三"重点基础研究发展规划基金项目(2004CB318108)
国家自然科学基金项目(60223004
60321002
60303005
60503064)
教育部科学技术研究基金重点项目(104236)
关键词
图像自动标注
多类分类器
空间映射
模型向量
automatic image annotation
multi-class classifier
space mapping
model-vector
