  Context plays an important role in visual pattern recognition as it provides
complementary clues for different learning tasks including image classification
and annotation. In the particular scenario of kernel learning, the general
recipe of context-based kernel design consists in learning positive
semi-definite similarity functions that return high values not only when data
share similar content but also similar context. However, in spite of having a
positive impact on performance, the use of context in these kernel design
methods has not been fully explored; indeed, context has been handcrafted
instead of being learned. In this paper, we introduce a novel context-aware
kernel design framework based on deep learning. Our method discriminatively
learns spatial geometric context as the weights of a deep network (DN). The
architecture of this network is fully determined by the solution of an
objective function that mixes content, context and regularization, while the
parameters of this network determine the most relevant (discriminant) parts of
the learned context. We apply this context and kernel learning framework to
image classification using the challenging ImageCLEF Photo Annotation
benchmark; the latter shows that our deep context learning provides highly
effective kernels for image classification as corroborated through extensive
experiments.
