2,972 research outputs found
Mid-level Deep Pattern Mining
Mid-level visual element discovery aims to find clusters of image patches
that are both representative and discriminative. In this work, we study this
problem from the prospective of pattern mining while relying on the recently
popularized Convolutional Neural Networks (CNNs). Specifically, we find that
for an image patch, activations extracted from the first fully-connected layer
of CNNs have two appealing properties which enable its seamless integration
with pattern mining. Patterns are then discovered from a large number of CNN
activations of image patches through the well-known association rule mining.
When we retrieve and visualize image patches with the same pattern,
surprisingly, they are not only visually similar but also semantically
consistent. We apply our approach to scene and object classification tasks, and
demonstrate that our approach outperforms all previous works on mid-level
visual element discovery by a sizeable margin with far fewer elements being
used. Our approach also outperforms or matches recent works using CNN for these
tasks. Source code of the complete system is available online.Comment: Published in Proc. IEEE Conf. Computer Vision and Pattern Recognition
201
Exemplar-based Linear Discriminant Analysis for Robust Object Tracking
Tracking-by-detection has become an attractive tracking technique, which
treats tracking as a category detection problem. However, the task in tracking
is to search for a specific object, rather than an object category as in
detection. In this paper, we propose a novel tracking framework based on
exemplar detector rather than category detector. The proposed tracker is an
ensemble of exemplar-based linear discriminant analysis (ELDA) detectors. Each
detector is quite specific and discriminative, because it is trained by a
single object instance and massive negatives. To improve its adaptivity, we
update both object and background models. Experimental results on several
challenging video sequences demonstrate the effectiveness and robustness of our
tracking algorithm.Comment: ICIP201
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