11,637 research outputs found
Supervised learning on graphs of spatio-temporal similarity in satellite image sequences
High resolution satellite image sequences are multidimensional signals
composed of spatio-temporal patterns associated to numerous and various
phenomena. Bayesian methods have been previously proposed in (Heas and Datcu,
2005) to code the information contained in satellite image sequences in a graph
representation using Bayesian methods. Based on such a representation, this
paper further presents a supervised learning methodology of semantics
associated to spatio-temporal patterns occurring in satellite image sequences.
It enables the recognition and the probabilistic retrieval of similar events.
Indeed, graphs are attached to statistical models for spatio-temporal
processes, which at their turn describe physical changes in the observed scene.
Therefore, we adjust a parametric model evaluating similarity types between
graph patterns in order to represent user-specific semantics attached to
spatio-temporal phenomena. The learning step is performed by the incremental
definition of similarity types via user-provided spatio-temporal pattern
examples attached to positive or/and negative semantics. From these examples,
probabilities are inferred using a Bayesian network and a Dirichlet model. This
enables to links user interest to a specific similarity model between graph
patterns. According to the current state of learning, semantic posterior
probabilities are updated for all possible graph patterns so that similar
spatio-temporal phenomena can be recognized and retrieved from the image
sequence. Few experiments performed on a multi-spectral SPOT image sequence
illustrate the proposed spatio-temporal recognition method
Content-based Video Retrieval by Integrating Spatio-Temporal and Stochastic Recognition of Events
As amounts of publicly available video data grow the need to query this data efficiently becomes significant. Consequently content-based retrieval of video data turns out to be a challenging and important problem. We address the specific aspect of inferring semantics automatically from raw video data. In particular, we introduce a new video data model that supports the integrated use of two different approaches for mapping low-level features to high-level concepts. Firstly, the model is extended with a rule-based approach that supports spatio-temporal formalization of high-level concepts, and then with a stochastic approach. Furthermore, results on real tennis video data are presented, demonstrating the validity of both approaches, as well us advantages of their integrated us
Search Tracker: Human-derived object tracking in-the-wild through large-scale search and retrieval
Humans use context and scene knowledge to easily localize moving objects in
conditions of complex illumination changes, scene clutter and occlusions. In
this paper, we present a method to leverage human knowledge in the form of
annotated video libraries in a novel search and retrieval based setting to
track objects in unseen video sequences. For every video sequence, a document
that represents motion information is generated. Documents of the unseen video
are queried against the library at multiple scales to find videos with similar
motion characteristics. This provides us with coarse localization of objects in
the unseen video. We further adapt these retrieved object locations to the new
video using an efficient warping scheme. The proposed method is validated on
in-the-wild video surveillance datasets where we outperform state-of-the-art
appearance-based trackers. We also introduce a new challenging dataset with
complex object appearance changes.Comment: Under review with the IEEE Transactions on Circuits and Systems for
Video Technolog
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Tracing the German Centennial Flood in the Stream of Tweets: First Lessons Learned
Social microblogging services such as Twitter result in massive streams of georeferenced messages and geolocated status updates. This real-time source of information is invaluable for many application areas, in particular for disaster detection and response scenarios. Consequently, a considerable number of works has dealt with issues of their acquisition, analysis and visualization. Most of these works not only assume an appropriate percentage of georeferenced messages that allows for detecting relevant events for a specific region and time frame, but also that these geolocations are reasonably correct in representing places and times of the underlying spatio-temporal situation. In this paper, we review these two key assumption based on the results of applying a visual analytics approach to a dataset of georeferenced Tweets from Germany over eight months witnessing several large-scale flooding situations throughout the country. Our results con rm the potential of Twitter as a distributed 'social sensor' but at the same time highlight some caveats in interpreting immediate results. To overcome these limits we explore incorporating evidence from other data sources including further social media and mobile phone network metrics to detect, confirm and refine events with respect to location and time. We summarize the lessons learned from our initial analysis by proposing recommendations and outline possible future work directions
Twofold Video Hashing with Automatic Synchronization
Video hashing finds a wide array of applications in content authentication,
robust retrieval and anti-piracy search. While much of the existing research
has focused on extracting robust and secure content descriptors, a significant
open challenge still remains: Most existing video hashing methods are fallible
to temporal desynchronization. That is, when the query video results by
deleting or inserting some frames from the reference video, most existing
methods assume the positions of the deleted (or inserted) frames are either
perfectly known or reliably estimated. This assumption may be okay under
typical transcoding and frame-rate changes but is highly inappropriate in
adversarial scenarios such as anti-piracy video search. For example, an illegal
uploader will try to bypass the 'piracy check' mechanism of YouTube/Dailymotion
etc by performing a cleverly designed non-uniform resampling of the video. We
present a new solution based on dynamic time warping (DTW), which can implement
automatic synchronization and can be used together with existing video hashing
methods. The second contribution of this paper is to propose a new robust
feature extraction method called flow hashing (FH), based on frame averaging
and optical flow descriptors. Finally, a fusion mechanism called distance
boosting is proposed to combine the information extracted by DTW and FH.
Experiments on real video collections show that such a hash extraction and
comparison enables unprecedented robustness under both spatial and temporal
attacks.Comment: submitted to Image Processing (ICIP), 2014 21st IEEE International
Conference o
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