基于空间矢量模型的图像分类方法

Image classification methods based on space vector model

针对词袋模型视觉单词没有考虑空间信息的不足,提出了一种基于视觉单词间空间位置信息的空间矢量模型。该模型利用视觉单词的空间位置信息,采用图像空间矢量模型对图像进行表述,从而达到了更好的分类效果。实验在两个标准图像数据集Caltech-101和Caltech-256上进行,分别采用支持向量机(SVM)和K最近邻分类器(KNN)对其进行分类。实验表明,空间矢量模型有效地提高了平均分类正确率(ACA)和平均类别准确率(ACP),具有很好的分类效果。

A space vector model is proposed to overcome the lack of spatial location information in the bag-of-words model.The model turns visual words into vector model using the space location information of visual words to represent image,thereby achieves better classification performance.Experiments are carried out on two standard image datasets Caltech-101 and Caltech-256,respectively,with Support Vector Machine（SVM）and K-Nearest Neighbor（KNN）classifiers.The results show that the space vector model can effectively improve the Average Classification Accuracy（ACA）and Average Category Precision（ACP）,and has a good classification effect.