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Results 1 - 10
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660
An extensive empirical study of feature selection metrics for text classification
- J. of Machine Learning Research
, 2003
"... Machine learning for text classification is the cornerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and more accurate. This paper presents an empirical comparison ..."
Abstract
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Cited by 496 (15 self)
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Machine learning for text classification is the cornerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and more accurate. This paper presents an empirical comparison
Support vector machine learning for interdependent and structured output spaces
- In ICML
, 2004
"... Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernel-based methods has focused on designing flexible and powerful input representations. This paper addresses the complementary issue of problems involving complex outputs suchas multiple depe ..."
Abstract
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Cited by 450 (20 self)
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dependent output variables and structured output spaces. We propose to generalize multiclass Support Vector Machine learning in a formulation that involves features extracted jointly from inputs and outputs. The resulting optimization problem is solved efficiently by a cutting plane algorithm that exploits
Efficient PSD Constrained Asymmetric Metric Learning for Person Re-identification
"... Person re-identification is becoming a hot research topic due to its value in both machine learning research and video surveillance applications. For this challenging problem, distance metric learning is shown to be effective in match-ing person images. However, existing approaches either re-quire a ..."
Abstract
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-ations. The proposed algorithm termed MLAPG is shown to be computationally efficient and able to perform low rank selection. We applied the proposed method for person re-identification, achieving state-of-the-art performance on four challenging databases (VIPeR, QMUL GRID, CUHK Campus, and CUHK03
Domain Transfer for Person Re-identification
"... Automatic person re-identification in is a crucial capability underpinning many applications in public space video surveillance. It is challenging due to intra-class variation in person appearance when observed in different views, together with limited inter-class variability. Various recent approac ..."
Abstract
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Cited by 4 (3 self)
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approaches have made great progress in re-identification performance using discriminative learning techniques. However, these approaches are fundamentally limited by the requirement of extensive annotated training data for every pair of views. For practical re-identification, this is an unreasonable
T.: The re-identification challenge
- In: Person Re-Identification
, 2014
"... Abstract For making sense of the vast quantity of visual data generated by the rapid expansion of large scale distributed multi-camera systems, automated per-son re-identification is essential. However, it poses a significant challenge to com-puter vision systems. Fundamentally, person re-identifica ..."
Abstract
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Cited by 6 (5 self)
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and health-care. As a result, the field has drawn growing and wide interest from academic researchers and industrial developers. This chapter introduces the re-identification problem, highlights the difficulties in building person re-identification systems, and presents an overview of recent progress
Person re-identification by pose priors
"... The person re-identification problem is a well known retrieval task that requires finding a person of interest in a network of cameras. In a real-world scenario, state of the art algorithms are likely to fail due to serious perspective and pose changes as well as variations in lighting conditions ac ..."
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The person re-identification problem is a well known retrieval task that requires finding a person of interest in a network of cameras. In a real-world scenario, state of the art algorithms are likely to fail due to serious perspective and pose changes as well as variations in lighting conditions
On the Exploration of Joint Attribute Learning for Person Re-identification
"... Abstract. This paper presents an algorithm for jointly learning a set of mid-level attributes from an image ensemble by locating clusters of de-pendent attributes. Human describable attributes are an active research topic due to their ability to transfer between domains, human under-standing, and im ..."
Abstract
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Cited by 1 (0 self)
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of performance improvement. We evaluate the joint learning algorithm on a set of attributes for the task of person re-identification. We find that the proposed algorithm can improve classifier accuracy over both independent or fully joint attribute classification. Furthermore, the enhanced classifiers also
Learning to rank in person re-identification with metric ensembles
"... We propose an effective structured learning based ap-proach to the problem of person re-identification which out-performs the current state-of-the-art on most benchmark data sets evaluated. Our framework is built on the ba-sis of multiple low-level hand-crafted and high-level vi-sual features. We th ..."
Abstract
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We propose an effective structured learning based ap-proach to the problem of person re-identification which out-performs the current state-of-the-art on most benchmark data sets evaluated. Our framework is built on the ba-sis of multiple low-level hand-crafted and high-level vi-sual features. We
Person Re-Identification by Efficient Impostor-based Metric Learning ∗
"... Recognizing persons over a system of disjunct cameras is a hard task for human operators and even harder for automated systems. In particular, realistic setups show difficulties such as different camera angles or different camera properties. Additionally, also the appearance of exactly the same pers ..."
Abstract
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Cited by 19 (0 self)
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person can change dramatically due to different views (e.g., frontal/back) of carried objects. In this paper, we mainly address the first problem by learning the transition from one camera to the other. This is realized by learning a Mahalanobis metric using pairs of labeled samples from different
S.Z.: Open-set person re-identification
, 2014
"... Abstract. Person re-identification is becoming a hot research for devel-oping both machine learning algorithms and video surveillance applica-tions. The task of person re-identification is to determine which person in a gallery has the same identity to a probe image. This task basically as-sumes tha ..."
Abstract
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Cited by 1 (0 self)
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Abstract. Person re-identification is becoming a hot research for devel-oping both machine learning algorithms and video surveillance applica-tions. The task of person re-identification is to determine which person in a gallery has the same identity to a probe image. This task basically as
Results 1 - 10
of
660
