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Results 11 - 20
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660
DENSE APPEARANCE MODELING AND EFFICIENT LEARNING OF CAMERA TRANSITIONS FOR PERSON RE-IDENTIFICATION
"... One central task in many visual surveillance scenarios is person re-identification, i.e., recognizing an individual person across a network of spatially disjoint cameras. Most successful recognition approaches are either based on direct modeling of the human appearance or on machine learning. In thi ..."
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One central task in many visual surveillance scenarios is person re-identification, i.e., recognizing an individual person across a network of spatially disjoint cameras. Most successful recognition approaches are either based on direct modeling of the human appearance or on machine learning
Remote Feature Learning for Mobile Re-Identification
- In International conference on Distributed Smart Cameras, Palm
, 2013
"... Abstract—This work introduces a novel method for person re-identification using embedded smart cameras. State-of-the-art methods address the re-identification problem using global and local features, metric learning and feature transformation algorithms. Such methods require advanced systems with hi ..."
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Cited by 4 (3 self)
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Abstract—This work introduces a novel method for person re-identification using embedded smart cameras. State-of-the-art methods address the re-identification problem using global and local features, metric learning and feature transformation algorithms. Such methods require advanced systems
CAN FEATURE-BASED INDUCTIVE TRANSFER LEARNING HELP PERSON RE-IDENTIFICATION?
"... Person re-identification concerns about the problem of recognizing people across space (captured by different cam-eras) and/or over time gaps. Though recently the literature on it grows rapidly, all the proposed solutions have treated it as a normal classification or ranking problem. In this paper, ..."
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Cited by 2 (1 self)
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method-ology: feature-based inductive transfer learning, for person re-identification. Extensive experiments on standard datasets with typical methods result in several important findings. Index Terms — Person re-identification, transfer learn-ing, inductive transfer learning, feature mapping
Person Re-Identification with Discriminatively Trained Viewpoint Invariant Dictionaries
"... This paper introduces a new approach to address the person re-identification problem in cameras with non-overlapping fields of view. Unlike previous approaches that learn Mahalanobis-like distance metrics in some trans-formed feature space, we propose to learn a dictionary that is capable of discrim ..."
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This paper introduces a new approach to address the person re-identification problem in cameras with non-overlapping fields of view. Unlike previous approaches that learn Mahalanobis-like distance metrics in some trans-formed feature space, we propose to learn a dictionary that is capable
Color Models and Weighted Covariance Estimation for Person Re-Identification
"... Abstract—Due to illumination changes, partial occlusions, and object scale differences, person re-identification over disjoint camera views becomes a challenging problem. To address this problem, a variety of image representations have been put forward. In this paper, the illumination invariance and ..."
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its feasibility and effectiveness. Finally, image pairs are matched based on the learned distance metric. Experiments conducted on two public benchmark datasets (VIPeR and PRID 450S) show that the proposed algorithm outperforms the state-of-the-art methods. Keywords—person re-identification
CHENG et al.: CUSTOM PICTORIAL STRUCTURES FOR RE-IDENTIFICATION 1 Custom Pictorial Structures for Re-identification
"... We propose a novel methodology for re-identification, based on Pictorial Structures (PS). Whenever face or other biometric information is missing, humans recognize an individual by selectively focusing on the body parts, looking for part-to-part correspondences. We want to take inspiration from this ..."
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Cited by 1 (0 self)
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on that specific person, leading to what we call a Custom Pictorial Structure (CPS). CPS learns the appearance of an individual, improving the localization of its parts, thus obtaining more reliable visual characteristics for re-identification. It is based on the statistical learning of pixel attributes collected
Learning Rules that Classify E-Mail
- Proceedings of the 1996 AAAI Spring Symposium on Machine Learning and Information Access
, 1996
"... wcohen~research.att.com Two methods for learning text classifiers are compared on classification problems that might arise in filtering and filing personM e-mail messages: a "traxiitionM IR " method based on TF-IDF weighting, and a new method for learning sets of "keyword-spotting rul ..."
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Cited by 198 (3 self)
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;keyword-spotting rules " based on the RIPPER rule learning algorithm. It is demonstrated that both methods obtain significant generalizations from a small number of examples; that both methods are comparable in generalization performance on problems of this type; and that both methods axe reasonably efficient, even
Multi-Shot Re-Identification with Random-Projection-Based Random Forests
"... Human re-identification remains one of the fundamen-tal, difficult problems in video surveillance and analysis. Current metric learning algorithms mainly focus on find-ing an optimized vector space such that observations of the same person in this space have a smaller distance than ob-servations of ..."
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Cited by 3 (3 self)
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Human re-identification remains one of the fundamen-tal, difficult problems in video surveillance and analysis. Current metric learning algorithms mainly focus on find-ing an optimized vector space such that observations of the same person in this space have a smaller distance than ob
A Contextual-Bandit Approach to Personalized News Article Recommendation
"... Personalized web services strive to adapt their services (advertisements, news articles, etc.) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamic ..."
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Cited by 178 (16 self)
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. The contributions of this work are three-fold. First, we propose a new, general contextual bandit algorithm that is computationally efficient and well motivated from learning theory. Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using
Trail re-identification: Learning who you are from where you have been
, 2003
"... This paper provides algorithms for learning the identities of individuals from the trails of seemingly anonymous information they leave behind. Consider online consumers, who have the IP addresses of their computers logged at each website visited. Many falsely believe they cannot be identified. The ..."
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Cited by 18 (1 self)
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extends the concept to “trail re-identification ” in which a person is related to a trail of seemingly anonymous and homogenous data left across different locations. The 3 novel algorithms presented in this paper perform trail re-identifications by exploiting the fact that some locations
Results 11 - 20
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660
