自动化学报2018，Vol.44Issue(1)：99-105,7.DOI:10.16383/j.aas.2018.c160041
一种基于视觉词典优化和查询扩展的图像检索方法
Image Retrieval with Enhanced Visual Dictionary and Query Expansion
摘要
Abstract
The most popular approach in image retrieval is based on the bag of visual-words(BoVW)model. However, there are several fundamental problems that restrict the performance of this method, such as low time efficiency, weak discrimination of visual words and less robustness. So, an image retrieval method with enhanced visual dictionary and query expansion is proposed. Firstly, clustering by fast search and finding density peaks are used to generate a group of visual words. Secondly, non-information words in the dictionary are eliminated by Chi-square model to improve the distinguishing ability of the visual dictionary. Finally, an efficient graph-based visual reranking method is introduced to refine the initial search results. Experimental results of Oxford5K and Paris6K datasets indicate that the expression ability of visual dictionary is effectively improved and the method is superior to the state-of-the-art image retrieval methods in performance.关键词
视觉词典模型/密度聚类/卡方模型/查询扩展Key words
Bag of visual words(BoVW)/clustering based on density/Chi-square model/query expansion引用本文复制引用
柯圣财,李弼程,陈刚,赵永威,魏晗..一种基于视觉词典优化和查询扩展的图像检索方法[J].自动化学报,2018,44(1):99-105,7.基金项目
国家自然科学基金(60872142),华侨大学科研基金资助Supported by National Natural Science Foundation of China(60872142)and Scientific Research Funds of Huaqiao University本文责任编委刘跃虎 （60872142）
