PatternNet: Visual Pattern Mining with Deep Neural Network

Visual patterns represent the discernible regularity in the visual
world. They capture the essential nature of visual objects or scenes.
Understanding and modeling visual patterns is a fundamental problem in
visual recognition that has wide ranging applications. In this paper, we
study the problem of visual pattern mining and propose a novel deep neural
network architecture called PatternNet for discovering these patterns
that are both discriminative and representative. The proposed PatternNet
leverages the filters in the last convolution layer of a convolutional
neural network to find locally consistent visual patches, and by combining
these filters we can effectively discover unique visual patterns.
In addition, PatternNet can discover visual patterns efficiently without
performing expensive image patch sampling, and this advantage provides
an order of magnitude speedup compared to most other approaches. We
evaluate the proposed PatternNet subjectively by showing randomly selected
visual patterns which are discovered by our method and quantitatively
by performing image classification with the identified visual
patterns and comparing our performance with the current state-of-theart.
We also directly evaluate the quality of the discovered visual patterns
by leveraging the identified patterns as proposed objects in an image and
compare with other relevant methods. Our proposed network and procedure,
PatterNet, is able to outperform competing methods for the tasks
described.