85 research outputs found
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
Fast Stochastic Hierarchical Bayesian MAP for Tomographic Imaging
Any image recovery algorithm attempts to achieve the highest quality
reconstruction in a timely manner. The former can be achieved in several ways,
among which are by incorporating Bayesian priors that exploit natural image
tendencies to cue in on relevant phenomena. The Hierarchical Bayesian MAP
(HB-MAP) is one such approach which is known to produce compelling results
albeit at a substantial computational cost. We look to provide further analysis
and insights into what makes the HB-MAP work. While retaining the proficient
nature of HB-MAP's Type-I estimation, we propose a stochastic
approximation-based approach to Type-II estimation. The resulting algorithm,
fast stochastic HB-MAP (fsHBMAP), takes dramatically fewer operations while
retaining high reconstruction quality. We employ our fsHBMAP scheme towards the
problem of tomographic imaging and demonstrate that fsHBMAP furnishes promising
results when compared to many competing methods.Comment: 5 Pages, 4 Figures, Conference (Accepted to Asilomar 2017
Deep Network for Simultaneous Decomposition and Classification in UWB-SAR Imagery
Classifying buried and obscured targets of interest from other natural and
manmade clutter objects in the scene is an important problem for the U.S. Army.
Targets of interest are often represented by signals captured using
low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar
(SAR) technology. This technology has been used in various applications,
including ground penetration and sensing-through-the-wall. However, the
technology still faces a significant issues regarding low-resolution SAR
imagery in this particular frequency band, low radar cross sections (RCS),
small objects compared to radar signal wavelengths, and heavy interference. The
classification problem has been firstly, and partially, addressed by sparse
representation-based classification (SRC) method which can extract noise from
signals and exploit the cross-channel information. Despite providing potential
results, SRC-related methods have drawbacks in representing nonlinear relations
and dealing with larger training sets. In this paper, we propose a Simultaneous
Decomposition and Classification Network (SDCN) to alleviate noise inferences
and enhance classification accuracy. The network contains two jointly trained
sub-networks: the decomposition sub-network handles denoising, while the
classification sub-network discriminates targets from confusers. Experimental
results show significant improvements over a network without decomposition and
SRC-related methods
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