12,041 research outputs found
Video Stream Retrieval of Unseen Queries using Semantic Memory
Retrieval of live, user-broadcast video streams is an under-addressed and
increasingly relevant challenge. The on-line nature of the problem requires
temporal evaluation and the unforeseeable scope of potential queries motivates
an approach which can accommodate arbitrary search queries. To account for the
breadth of possible queries, we adopt a no-example approach to query retrieval,
which uses a query's semantic relatedness to pre-trained concept classifiers.
To adapt to shifting video content, we propose memory pooling and memory
welling methods that favor recent information over long past content. We
identify two stream retrieval tasks, instantaneous retrieval at any particular
time and continuous retrieval over a prolonged duration, and propose means for
evaluating them. Three large scale video datasets are adapted to the challenge
of stream retrieval. We report results for our search methods on the new stream
retrieval tasks, as well as demonstrate their efficacy in a traditional,
non-streaming video task.Comment: Presented at BMVC 2016, British Machine Vision Conference, 201
Computational Topology Techniques for Characterizing Time-Series Data
Topological data analysis (TDA), while abstract, allows a characterization of
time-series data obtained from nonlinear and complex dynamical systems. Though
it is surprising that such an abstract measure of structure - counting pieces
and holes - could be useful for real-world data, TDA lets us compare different
systems, and even do membership testing or change-point detection. However, TDA
is computationally expensive and involves a number of free parameters. This
complexity can be obviated by coarse-graining, using a construct called the
witness complex. The parametric dependence gives rise to the concept of
persistent homology: how shape changes with scale. Its results allow us to
distinguish time-series data from different systems - e.g., the same note
played on different musical instruments.Comment: 12 pages, 6 Figures, 1 Table, The Sixteenth International Symposium
on Intelligent Data Analysis (IDA 2017
Hellinger Distance Trees for Imbalanced Streams
Classifiers trained on data sets possessing an imbalanced class distribution
are known to exhibit poor generalisation performance. This is known as the
imbalanced learning problem. The problem becomes particularly acute when we
consider incremental classifiers operating on imbalanced data streams,
especially when the learning objective is rare class identification. As
accuracy may provide a misleading impression of performance on imbalanced data,
existing stream classifiers based on accuracy can suffer poor minority class
performance on imbalanced streams, with the result being low minority class
recall rates. In this paper we address this deficiency by proposing the use of
the Hellinger distance measure, as a very fast decision tree split criterion.
We demonstrate that by using Hellinger a statistically significant improvement
in recall rates on imbalanced data streams can be achieved, with an acceptable
increase in the false positive rate.Comment: 6 Pages, 2 figures, to be published in Proceedings 22nd International
Conference on Pattern Recognition (ICPR) 201
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