计算机工程与应用2011，Vol.47Issue(31)：148-150,3.DOI:10.3778/j.issn.1002-8331.2011.31.042
利用模型选择确定视觉词袋模型中词汇数目
Determine word number of Visual Bag-of-Words model by model selection method
摘要
Abstract
Visual Bag-of-Words model has been widely used in image classification, retrieval and recognition.However, its word number usually is selected by user experience or determined using the supervised cross-validation scheme.In this paper, an unsupervised method is proposed to infer the word number of Visual Bag-of-Words model (BoW) based on the idea of model selection.Firstly, Gaussian Mixture Models (GMM) are built accounting for BoWs with different word number.After-wards,Bayesian Information Criterion(BIC) is adopted to select the best model that has the minimum BIC value.Compared with cross-validation approach using image classification, the result demonstrates the effectiveness of the proposed approach.关键词
视觉词袋模型/模型选择/高斯混合模型/贝叶斯信息准则Key words
Visual Bag-of-Words/model selection/Gaussian Mixture Model(GMM)/Bayesian information criterion分类
信息技术与安全科学引用本文复制引用
许明,韩军伟,郭雷,尹文杰..利用模型选择确定视觉词袋模型中词汇数目[J].计算机工程与应用,2011,47(31):148-150,3.基金项目
国家自然科学基金(the National Natural Science Foundation of China under Grant No.61005018) （the National Natural Science Foundation of China under Grant No.61005018）
西北工业大学引进高层次人才科研启动费资助项目. （）
