基于支持向量域描述的图像集匹配

Image Set Matching Based on Support Vector Domain Description

提出一种基于支持向量域描述的图像集匹配方法.该方法首先通过支持向量机学习,将每个图像集合映射到高维特征空间,使用支持向量域对图像集合建模,建立的模型使用一个包含大部分样本的最小闭球表示.然后引入基于支持向量域之间距离的相似性度量,将集合的匹配转换为成对的支持向量域之间的距离计算.最后在基于集合的人脸和对象识别任务中分别进行测试,文中方法的识别率在ETH80、HondaUCSD和YouTube数据库上分别达到96.37%、100%和95.32%,优于其他方法.

An image set matching method based on support vector domain description is proposed. Firstly, each image set from the original input space is mapped into the high dimensional feature space by support vector machine learning, and then they are modeled using support vector domain description. In feature space, the model is described by a smallest enclosing ball, which encloses the most of the mapped data. Next, by introducing an efficient similarity metric based on support vector domain, the distance between two image sets is converted to the distance between pairwise support vector domains. Finally, the proposed method is evaluated on face recognition and object classification tasks based on datasets. Experimental results show that the proposed method outperforms other state-of-the-art set based matching methods. The recognition rates of the proposed method reaches 96. 37%, 100% and 95. 32% on ETH80 object database, HondaUCSD and YouTube video databases, respectively.