一种基于词袋模型的图像分类方法

摘要

The Bag of Words(BoW) model is applied to object classification. An improved algorithm based on soft quantification is proposed. This algorithm quantifies the Scale-Invariant Feature Transform (SIFT) descriptor into several nearest visual words and performs weighting on them, which can conserve the space information. Therefore, it can avoid the information loss of eigen space caused by hard quantification. The experiments are carried out on Caltech 101 database. Experimental results show that the proposed method performs better than traditional method on image classification.%采用词袋模型(BoW)对图像进行分类，并针对传统词袋模型存在的不足进行了改进，提出了一种特征软量化的方式。软赋值量化通过将局部显著特征量化(SIFT)为与其距离最近的若干个视觉单词，并对其进行加权，由此保存特征空间中的距离信息，从而解决硬赋值量化造成的特征空间信息损失问题。通过在Caltech 101数据库进行实验，验证了本文方法的有效性，实验结果表明，该方法能够大幅度提高图像分类的性能。