融合主题和视觉语义的图像自动标注方法  

Automatic Image Annotation by Combining Aspects and Visual Semantics

作　　者：赵鹏[1,2] 王文彬[1,2] 朱伟伟[1,2] 

机构地区：[1]安徽大学计算智能与信号处理教育部重点实验室,合肥230039 [2]安徽大学计算机科学与技术学院,合肥230601

出　　处：《计算机辅助设计与图形学学报》2013年第11期1709-1714,共6页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(61201413);安徽省教育厅重点项目(KJ2009A001Z);安徽省科技厅重大科技专项(08010201002);安徽大学青年骨干教师培养基金(33010017)

摘　　要：为了减小图像语义检索过程中"语义鸿沟"的影响,提出融合主题和视觉语义的图像自动标注方法.该方法先在训练集的文本空间中用概率潜在语义分析(PLSA)模型拟合出主题集合;然后根据图像的高维视觉特征建立主题集合中每个主题的高斯混合模型(GMM),以准确描述其视觉语义信息,减小了"语义鸿沟",提高了图像自动标注的准确性.在Corel数据集上进行了对比实验的结果表明,文中方法在标注的平均标准率和平均标全率上都表现良好,证明了其有效性.To reduce the influence of the semantic gap in image retrieval, this paper presents an automatic image annotation method combining aspects and visual semantics. This method captures the latent aspects from the textual space of the training image set using probabilistic latent semantic analysis model firstly. And then, Gaussian Mixture Model of the each latent aspect is constructed according to the high dimensional image visual feature, describing the visual semantic content of each aspect. This method reduces the semantic gap, and improves the accuracy of the automatic image annotation. This method is compared with several other state-of-the-art methods on the standard Corel dataset. The results of experiments show that this method achieves better average recall and better average precision. The effectiveness of this method has been proved.

关 键 词：高斯混合模型 概率潜在语义分析 图像自动标注 语义鸿沟 特征聚类 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]