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| March 13
| March 13
| Xavier Gibert Serra
| Xavier Gibert Serra
| TBA
| Anomaly Detection on Outdoor Images Using Sparse Representations
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| March 20
| March 20
We propose a Predictable Dual-View Hashing (PDH) algorithm which embeds proximity of data samples in the original spaces. We create a cross-view hamming space with the ability to compare information from previously incomparable domains with a notion of 'predictability'. By performing comparative experimental analysis on two large datasets, PASCAL-Sentence and SUN-Attribute, we demonstrate the superiority of our method to the state-of-the-art dual-view binary code learning algorithms. We also propose an unsupervised domain adaptation method that exploits intrinsic compact structures of categories across different domains using binary attributes. Our method directly optimizes for classification in the target domain. The key insight is finding attributes that are discriminative across categories and predictable across domains.
We propose a Predictable Dual-View Hashing (PDH) algorithm which embeds proximity of data samples in the original spaces. We create a cross-view hamming space with the ability to compare information from previously incomparable domains with a notion of 'predictability'. By performing comparative experimental analysis on two large datasets, PASCAL-Sentence and SUN-Attribute, we demonstrate the superiority of our method to the state-of-the-art dual-view binary code learning algorithms. We also propose an unsupervised domain adaptation method that exploits intrinsic compact structures of categories across different domains using binary attributes. Our method directly optimizes for classification in the target domain. The key insight is finding attributes that are discriminative across categories and predictable across domains.
===Anomaly Detection on Outdoor Images Using Sparse Representations===
Speaker: [http://www.umiacs.umd.edu/~gibert/ Xavier Gibert Serra] -- Date: March 13, 2014
The integrity of safety-critical infrastructure, such as railway tracks, roads, or bridges needs to be monitored regularly to prevent catastrophic failures. For example, federal regulations require visual inspection of all high speed tracks twice each week. Traditional manual inspection methods are time-consuming and prone to human error. With the availability of high-speed cameras, it is possible to survey large areas in less time. However, detecting cracks and other anomalies on these images is a particularly challenging problem because of the uncontrolled environment arising from differences in material composition, and superficial degradation caused by outdoor elements. Due to speed requirements, images acquired from a moving vehicle have limited resolution, causing the smallest of these cracks to be under-sampled in the transversal dimension. Therefore, these cracks get mixed with background texture, resulting in negative signal-to-noise ratio. State-of-the art methods are based on linear filters, which are only optimal under additive Gaussian noise assumptions. This problem of simultaneous detection and clustering of anomalies in textured images can be posed as a blind source separation problem, and by exploiting the mutual incoherence of the dictionaries of shearlets and isotropic wavelets, which sparsely represent cracks and texture, we can separate each component using an iterative shrinkage algorithm. In this talk, I will present an integrated framework for image separation, feature extraction, clustering and classification that takes advantage of this decomposition.