三角形约束下的词袋模型图像分类方法

摘要

Bag of visual words model is widely used in image classification and image retrieval.In traditional bag of words model,the statistical method of visual words ignores the spatial information and object shape information,resulting lack of ability to distinguish between image features.In this paper,an improved bag of words method is proposed to combine with salient region extraction and visual words topological structure so that it is can not only produce more representative visual words to certain extent,but also avoid the disturbance of complex background information and position change.First of all,the significant areas of training image are extracted and the bag of visual words model is built on the significant area.Secondly,in order to describe the characteristics of the image more accurately and resist the changing location and the influence of background information,the strategies of visual words topological structure and Delaunay triangulation method are utilized and integrated into the global information and local information.Simulation experiments are performed to compare with the traditional bag of words and other models,the results demonstrate that the proposed method obtained a higher classification accuracy.%视觉词袋模型广泛地应用于图像分类与图像检索等领域.在传统词袋模型中,视觉单词统计方法忽略了视觉词之间的空间信息以及分类对象形状信息,导致图像特征表示区分能力不足.提出了一种改进的视觉词袋方法,结合显著区域提取和视觉单词拓扑结构,不仅能够产生更具代表性的视觉单词,而且能够在一定程度上避免复杂背景信息和位置变化带来的干扰.首先,通过对训练图像进行显著区域提取,在得到的显著区域上构建视觉词袋模型.其次,为了更精确地描述图像的特征,抵抗多变的位置和背景信息的影响,该方法采用视觉单词拓扑结构策略和三角剖分方法,融入全局信息和局部信息.通过仿真实验,并与传统的词袋模型及其他模型进行比较,结果表明,该方法获得了更高的分类准确率.