5,039 research outputs found
Ancient Coin Classification Using Graph Transduction Games
Recognizing the type of an ancient coin requires theoretical expertise and
years of experience in the field of numismatics. Our goal in this work is
automatizing this time consuming and demanding task by a visual classification
framework. Specifically, we propose to model ancient coin image classification
using Graph Transduction Games (GTG). GTG casts the classification problem as a
non-cooperative game where the players (the coin images) decide their
strategies (class labels) according to the choices made by the others, which
results with a global consensus at the final labeling. Experiments are
conducted on the only publicly available dataset which is composed of 180
images of 60 types of Roman coins. We demonstrate that our approach outperforms
the literature work on the same dataset with the classification accuracy of
73.6% and 87.3% when there are one and two images per class in the training
set, respectively
Dynamic Metric Learning from Pairwise Comparisons
Recent work in distance metric learning has focused on learning
transformations of data that best align with specified pairwise similarity and
dissimilarity constraints, often supplied by a human observer. The learned
transformations lead to improved retrieval, classification, and clustering
algorithms due to the better adapted distance or similarity measures. Here, we
address the problem of learning these transformations when the underlying
constraint generation process is nonstationary. This nonstationarity can be due
to changes in either the ground-truth clustering used to generate constraints
or changes in the feature subspaces in which the class structure is apparent.
We propose Online Convex Ensemble StrongLy Adaptive Dynamic Learning (OCELAD),
a general adaptive, online approach for learning and tracking optimal metrics
as they change over time that is highly robust to a variety of nonstationary
behaviors in the changing metric. We apply the OCELAD framework to an ensemble
of online learners. Specifically, we create a retro-initialized composite
objective mirror descent (COMID) ensemble (RICE) consisting of a set of
parallel COMID learners with different learning rates, demonstrate RICE-OCELAD
on both real and synthetic data sets and show significant performance
improvements relative to previously proposed batch and online distance metric
learning algorithms.Comment: to appear Allerton 2016. arXiv admin note: substantial text overlap
with arXiv:1603.0367
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