18,437 research outputs found
DART: Distribution Aware Retinal Transform for Event-based Cameras
We introduce a generic visual descriptor, termed as distribution aware
retinal transform (DART), that encodes the structural context using log-polar
grids for event cameras. The DART descriptor is applied to four different
problems, namely object classification, tracking, detection and feature
matching: (1) The DART features are directly employed as local descriptors in a
bag-of-features classification framework and testing is carried out on four
standard event-based object datasets (N-MNIST, MNIST-DVS, CIFAR10-DVS,
NCaltech-101). (2) Extending the classification system, tracking is
demonstrated using two key novelties: (i) For overcoming the low-sample problem
for the one-shot learning of a binary classifier, statistical bootstrapping is
leveraged with online learning; (ii) To achieve tracker robustness, the scale
and rotation equivariance property of the DART descriptors is exploited for the
one-shot learning. (3) To solve the long-term object tracking problem, an
object detector is designed using the principle of cluster majority voting. The
detection scheme is then combined with the tracker to result in a high
intersection-over-union score with augmented ground truth annotations on the
publicly available event camera dataset. (4) Finally, the event context encoded
by DART greatly simplifies the feature correspondence problem, especially for
spatio-temporal slices far apart in time, which has not been explicitly tackled
in the event-based vision domain.Comment: 12 pages, revision submitted to TPAMI in Nov 201
Online Product Quantization
Approximate nearest neighbor (ANN) search has achieved great success in many
tasks. However, existing popular methods for ANN search, such as hashing and
quantization methods, are designed for static databases only. They cannot
handle well the database with data distribution evolving dynamically, due to
the high computational effort for retraining the model based on the new
database. In this paper, we address the problem by developing an online product
quantization (online PQ) model and incrementally updating the quantization
codebook that accommodates to the incoming streaming data. Moreover, to further
alleviate the issue of large scale computation for the online PQ update, we
design two budget constraints for the model to update partial PQ codebook
instead of all. We derive a loss bound which guarantees the performance of our
online PQ model. Furthermore, we develop an online PQ model over a sliding
window with both data insertion and deletion supported, to reflect the
real-time behaviour of the data. The experiments demonstrate that our online PQ
model is both time-efficient and effective for ANN search in dynamic large
scale databases compared with baseline methods and the idea of partial PQ
codebook update further reduces the update cost.Comment: To appear in IEEE Transactions on Knowledge and Data Engineering
(DOI: 10.1109/TKDE.2018.2817526
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