利用模型选择确定视觉词袋模型中词汇数目

摘要

视觉词袋(Visual Bag-of-Words)模型在图像分类、检索和识别等计算机视觉领域有了广泛的应用,但是视觉词袋模型中词汇数目往往是根据经验确定或者采用有监督的交叉学习选取.提出—种确定视觉词袋模型中词汇数目的无监督方法,利用模型选择的思想来解决问题.使用高斯混合模型描述具有不同词汇数目的视觉词袋,计算各模型贝叶斯信息准则的值,选取贝叶斯信息准则最小值对应的词汇数目.与交叉验证的监督学习在图像分类实验的对比结果说明该方法准确有效.%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.