Abstract 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.
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2014, Vol. 27 
