Abstract: Recently, bag-of-words (BoW) model has been approved by researchers due to their good performance. There are mainly two categories of bag-of-words models. One is to add spatial information into the image feature representation and the other is to add semantic information. This paper proposes an image feature representation method which combines the spatial information between feature points with the semantic information, and makes the feature show better performance. This paper extracts similar visual words by computing distribution divergence and forms visual phrase, which can present the meaning of image. Image classification experiments based on this method are conducted on UIUC-Sports8 dataset and Scene-15 dataset, and the results show that the visual phrase method has better classification accuracy compared with the conventional bag-of-words model and other models.

Key words: feature representation, bag-of-words model, visual word, visual phrase

摘要： 近年来词袋（bag-of-words，BoW）模型因为其较高的性能而被人们认可。词袋模型的改进方法主要包括两种，一种是在图像特征表示中加入空间信息，另一种是加入语义信息。研究了结合图像特征点间的空间和语义信息的高性能图像特征表示方法，通过计算图像中视觉词间的分布距离，并提取相似的视觉词组成视觉短语，来更好地表示图像。在UIUC-Sports8图像库和Scene-15图像库上进行图像分类实验，并与传统的词袋模型及其他模型进行比较，结果显示视觉词短语方法获得了更高的分类准确率。

关键词: 特征表示, 词袋模型, 视觉词, 视觉短语