一种基于随机化视觉词汇和聚类集成目标分类  

An object categorization approach based on randomized visual vocabulary and clustering aggregation

作　　者：朱道广[1] 李弼程[1] 

机构地区：[1]信息工程大学信息系统工程学院,河南郑州450002

出　　处：《太赫兹科学与电子信息学报》2014年第2期276-283,共8页Journal of Terahertz Science and Electronic Information Technology

摘　　要：针对传统的视觉词典法存在的时间复杂度高,视觉单词同义性、歧义性和高维局部特征聚类不稳定问题,提出了一种基于随机化视觉词汇和聚类集成的目标分类方法。采用精确欧式位置敏感哈希(E2LSH)对训练图像库的局部特征点进行哈希映射,生成一组随机化视觉词汇;然后,聚类集成这组随机化视觉词汇,构建随机化视觉词汇集成词典(RVVAD);最后,基于该词典构建图像的视觉单词直方图并使用支持向量机(SVM)分类器完成目标分类。实验结果表明,本文方法有效增强了词典的表达能力,提高了目标分类的准确率。Considering the problems with the conventional Bag-of-Visual-Words approaches, such as great time consumption, the synonymy and ambiguity of visual word, and instability of clustering high-dimensionality image local features, this paper presents a novel object categorization approach based on randomized visual vocabulary and clustering aggregation. Firstly, Exact Euclidean Locality Sensitive Hashing (E2LSH) is used to cluster local features of the training dataset, and a group of randomized visual vocabularies is constructed. Then, the randomized visual vocabularies are aggregated by using clustering aggregation technique, resulting in Randomized Visual Vocabularies Aggregating Dictionary(RVVAD). Finally, the visual words histogram is generated according to the dictionary, and the Support Vector Machines(SVM) are adopted to accomplish image object categorization. Experimental results indicate that the expression ability of the dictionary is effectively improved, and the object categorization precision is increased dramatically.

关 键 词：目标分类 聚类集成 精确欧式位置敏感哈希 随机化视觉词汇 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]