NDLI: Discriminant Tracking Using Tensor Representation with Semi-supervised Improvement

Content Provider	IEEE Xplore Digital Library
Author	Jin Gao Junliang Xing Weiming Hu Maybank, S.
Copyright Year	2013
Description	Author affiliation: Nat. Lab. of Pattern Recognition, Inst. of Autom., Beijing, China (Jin Gao; Junliang Xing; Weiming Hu) \|\| Dept. of Comput. Sci., Birkbeck Coll., London, UK (Maybank, S.)
Abstract	Visual tracking has witnessed growing methods in object representation, which is crucial to robust tracking. The dominant mechanism in object representation is using image features encoded in a vector as observations to perform tracking, without considering that an image is intrinsically a matrix, or a 2^nd-order tensor. Thus approaches following this mechanism inevitably lose a lot of useful information, and therefore cannot fully exploit the spatial correlations within the 2D image ensembles. In this paper, we address an image as a 2^nd-order tensor in its original form, and find a discriminative linear embedding space approximation to the original nonlinear sub manifold embedded in the tensor space based on the graph embedding framework. We specially design two graphs for characterizing the intrinsic local geometrical structure of the tensor space, so as to retain more discriminant information when reducing the dimension along certain tensor dimensions. However, spatial correlations within a tensor are not limited to the elements along these dimensions. This means that some part of the discriminant information may not be encoded in the embedding space. We introduce a novel technique called semi-supervised improvement to iteratively adjust the embedding space to compensate for the loss of discriminant information, hence improving the performance of our tracker. Experimental results on challenging videos demonstrate the effectiveness and robustness of the proposed tracker.
Sponsorship	IEEE Comput. Soc.
Starting Page	1569
Ending Page	1576
File Size	1142481
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.198
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tensile stress Vectors Training Visualization Correlation Optimization Robustness
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Semi-supervised learning and optimization for hypergraph matching

Object Tracking by Structure Tensor Analysis

Multi-target Tracking by Rank-1 Tensor Approximation

Multi-cue Visual Tracking Using Robust Feature-Level Fusion Based on Joint Sparse Representation

Ensemble Projection for Semi-supervised Image Classification

Face Recognition using Discriminatively Trained Orthogonal Rank One Tensor Projections

Boosting Incremental Semi-supervised Discriminant Analysis for Tracking

Beyond the graphs: Semi-parametric semi-supervised discriminant analysis

Robust Tucker Tensor Decomposition for Effective Image Representation

Discriminant Tracking Using Tensor Representation with Semi-supervised Improvement

Similar Documents

Semi-supervised learning and optimization for hypergraph matching

Object Tracking by Structure Tensor Analysis

Multi-target Tracking by Rank-1 Tensor Approximation

Multi-cue Visual Tracking Using Robust Feature-Level Fusion Based on Joint Sparse Representation

Ensemble Projection for Semi-supervised Image Classification

Face Recognition using Discriminatively Trained Orthogonal Rank One Tensor Projections

Boosting Incremental Semi-supervised Discriminant Analysis for Tracking

Beyond the graphs: Semi-parametric semi-supervised discriminant analysis

Robust Tucker Tensor Decomposition for Effective Image Representation

Discriminant Tracking Using Tensor Representation with Semi-supervised Improvement