一种新的基于频繁加权概念格的视觉单词生成方法  

A New Generation Method of Visual Words Based on Frequent weighted Concept Lattice

作　　者：褚萌[1] 张素兰[1] 张继福[1] 

机构地区：[1]太原科技大学计算机科学与技术学院,太原030024

出　　处：《太原科技大学学报》2012年第6期421-425,共5页Journal of Taiyuan University of Science and Technology

基　　金：山西省自然科学基金(2010011021-2);山西省回国留学人员科研资助项目(2009-77)

摘　　要：传统的视觉单词仅通过无监督聚类方法生成,标注的精度和效率较低。加权概念格是一种有效的层次数据分析工具,本文采用加权概念格对视觉单词进行分析与约简,提出了一种新的视觉单词生成方法。首先生成训练图像视觉词包的形式背景,并通过信息熵获取视觉单词的权值;其次针对各语义类别,根据用户所设定的内涵重要性阈值,构造出视觉词包模型频繁加权概念格;然后依据外延数阈值,提取对分类贡献大的描述图像语义的约简视觉单词,进一步提高了标注的精度和效率;最后通过实验验证了该方法是有效的和可行的。Traditional visual word is generated through unsupervised clustering method, and the image semantic classification performance is not high. Weighted concept lattice is an effective tool for data analysis. Through analy- zing and reducing visual words by making use of weighted concept lattice, the paper presents a novel method of building visual words. First, the formal context about the BOV of training image is generated, and the weight value of visual word is acquired through information entropy. Second, the frequent weighted concept lattice of Bag-of- vis- ual words model is constructed for each semantic category according to the intent threshold given by the user. Then, the reduced visual words which contribute to scene category were selected to construct visual words dictiona- ry according to the extent threshold, and further the precision and efficiency of label was improved. Final experi- ments show that it is effective and feasible.

关 键 词：频繁加权概念格 视觉词包 视觉单词约简 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]