BIROn - Birkbeck Institutional Research Online

Semi-supervised tensor-based graph embedding learning and its application to visual discriminant tracking

Hu, W. and Gao, J. and Xing, J. and Zhang, C. and Maybank, Stephen J. (2017) Semi-supervised tensor-based graph embedding learning and its application to visual discriminant tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (1), pp. 172-188. ISSN 0162-8828.

[img]
Preview
Text
14581.pdf - Author's Accepted Manuscript

Download (1MB) | Preview

Abstract

An appearance model adaptable to changes in object appearance is critical in visual object tracking. In this paper, we treat an image patch as a 2-order tensor which preserves the original image structure. We design two graphs for characterizing the intrinsic local geometrical structure of the tensor samples of the object and the background. Graph embedding is used to reduce the dimensions of the tensors while preserving the structure of the graphs. Then, a discriminant embedding space is constructed. We prove two propositions for finding the transformation matrices which are used to map the original tensor samples to the tensor-based graph embedding space. In order to encode more discriminant information in the embedding space, we propose a transfer-learningbased semi-supervised strategy to iteratively adjust the embedding space into which discriminative information obtained from earlier times is transferred. We apply the proposed semi-supervised tensor-based graph embedding learning algorithm to visual tracking. The new tracking algorithm captures an object’s appearance characteristics during tracking and uses a particle filter to estimate the optimal object state. Experimental results on the CVPR 2013 benchmark dataset demonstrate the effectiveness of the proposed tracking algorithm.

Metadata

Item Type: Article
Additional Information: (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Keyword(s) / Subject(s): Discriminant tracking, Tensor samples, Semi-supervised learning, Graph embedding space
School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Depositing User: Administrator
Date Deposited: 19 Apr 2016 11:15
Last Modified: 11 Apr 2025 21:44
URI: https://eprints.bbk.ac.uk/id/eprint/14581

Statistics

6 month trend
730Downloads
6 month trend
266Hits

Additional statistics are available via IRStats2.

Archive Staff Only (login required)

Edit/View Item
Edit/View Item