Abstract
Image-set classification has recently generated great
popularity due to its widespread applications in computer
vision. The great challenges arise from effectively and ef-
ficiently measuring the similarity between image sets with
high inter-class ambiguity and huge intra-class variability.
In this paper, we propose deep match kernels (DMK) to
directly measure the similarity between image sets in the
match kernel framework. Specifically, we build deep local match kernels between images upon arc-cosine kernels,
which can faithfully characterize the similarity between images by mimicking deep neural networks; we introduce anchors to aggregate those deep local match kernels into a
global match kernel between image sets, which is learned
in a supervised way by kernel alignment and therefore more
discriminative. The DMK provides the first match kernel
framework for image-set classification, which removes specific assumptions usually required in previous approaches
and is computationally more efficient. We conduct extensive
experiments on four datasets for three diverse image-set
classification tasks. The DMK achieves high performance
and consistently surpasses state-of-the-art methods, showing its great effectiveness for image-set classification