计算机工程2011，Vol.37Issue(19)：204-206,209,4.DOI:10.3969/j.issn.1000-3428.2011.19.067
词包模型中视觉单词歧义性分析
Visual Words Ambiguity Analysis in BOW Model
摘要
Abstract
Visual words in the traditional Bag of Word(BOW) model can be gotten by an unsupervised method of clustering the visual features. But one critical limitation of existing BOW is not concerned with the semantic natures of visual words. This paper proposes a visual wordsambiguity analysis method based on text categorization. The codebook is generated by the BOW model. There are three ways of analysis-document frequency, ^distribution and information gains, and then they reduce the low information visual words after analyzing. It gets optimized visual words, the histogram formed by the frequency of visual words is used in image categorization task by the Support Vector Machine(SVM) classifier. Experimental results show that this method has higher classification accuracy.关键词
图像分类/视觉单词/文本分类/支持向量机/词包模型Key words
image classification/ visual words/ text classification/ Support Vector Machine(SVM)/ Bag of Word(BOW) model分类
信息技术与安全科学引用本文复制引用
刘扬闻,霍宏,方涛..词包模型中视觉单词歧义性分析[J].计算机工程,2011,37(19):204-206,209,4.基金项目
国家“973”计划基金资助项目(2006CB701303) （2006CB701303）
国家自然科学基金资助项目(41071256) （41071256）
