Image set matching has attracted increasing attention in the field of pattern recognition. For set-based image matching

the key issues can be categorized on the basis of the processes of representing the image set and measuring the similarity between two sets. Support vector domain description (SVDD) is a recently developed method based on support vector machine learning. SVDD is a boundary one-class learning method that maximizes the availability of samples that do not belong to the target class in refining its decision boundary

and can be used to describe a set of objects. Accordingly

each image set is described with a hypersphere

and the problem of image set matching is converted into the measure of the distance between two hyperspheres. Using support vector machine learning

each image set from the original input space is mapped into a high-dimensional feature space and modeled with support vector domain to handle the underlying non-linearity in the data space. In the feature space

a hypersphere encloses most of the mapped data. Thereafter

a novel metric is proposed based on domain–domain distance in a high-dimensional feature space; the distance between two image sets is then converted into the distance between pair-wise domains. However

the SVDD model has a disadvantageously simple form with only a single kernel information. Selecting the best kernel parameters is difficult and the constructed hypersphere is considerably sensitive to the trade-off parameter. Multiple kernel learning methods apply multiple kernels instead of merely one specific kernel function and its corresponding parameters. Recent developments in composition kernel learning for classification motivated us to apply a position-based weighting instead of the same global trade-off parameter to discriminate the importance of samples. Furthermore

considering the SVDD model's disadvantageously simple form with only one kernel and the difficulty of selecting the best kernel parameters

we propose a multi-kernel SVDD model

which can flexibly describe the data distribution boundary in the feature space after analyzing the space of multi-kernel mapping. This study utilizes the nearest neighbor classifier to obtain the class label. This study's experimental settings reach 100%

98.72%

and 62.34% recognition rate in the public Honda/UCSD

CMU MoBo

and YouTube video database

respectively. Given that multi-kernel learning can improve the efficiency of kernel selection and automatically evaluate the relative importance of the candidate kernels

the multi-kernel SVDD model flexibly describes the data distribution boundary in the feature space and provides a considerably accurate data description for the multifaceted context of the multi-model data set. Experiments conducted on public data sets demonstrate that the multi-kernel SVDD improves prediction accuracy and assists in characterizing the properties of complex data.