计算机工程与应用2016，Vol.52Issue(23)：181-184,219,5.DOI:10.3778/j.issn.1002-8331.1502-0128
基于最大频繁项集的图像分类技术
Image categorization based on maximum frequent item-sets
摘要
Abstract
An improved Bag Of Visual Words(BOVW)representation algorithm based on maximum frequent item-sets is proposed. Isolated points are ruled out and an efficient mining of maximum frequent item-sets based on annular region division is used to find visual words occurring frequently. The proposed algorithm highlights the differential features between different categories and spatial information is contained. In comparison, traditional BOVW could not fully express image common characteristics on one category. Experimental results on COREL and Caltech-256 database demonstrate the effectiveness and feasibility of proposed algorithm.关键词
图像分类/视觉单词/最大频繁项集Key words
image categorization/visual words/maximum frequent sets分类
信息技术与安全科学引用本文复制引用
朱书眉,王诚..基于最大频繁项集的图像分类技术[J].计算机工程与应用,2016,52(23):181-184,219,5.基金项目
国家自然科学基金（No.61071167）。 （）
