IEICE TRANSACTIONS on Information
Manifold Kernel Metric Learning for Larger-Scale Image Annotation
Summary :
An appropriate similarity measure between images is one of the key techniques in search-based image annotation models. In order to capture the nonlinear relationships between visual features and image semantics, many kernel distance metric learning(KML) algorithms have been developed. However, when challenged with large-scale image annotation, their metrics can't explicitly represent the similarity between image semantics, and their algorithms suffer from high computation cost. Therefore, they always lose their efficiency. In this paper, we propose a manifold kernel metric learning (M_KML) algorithm. Our M_KML algorithm will simultaneously learn the manifold structure and the image annotation metrics. The main merit of our M_KML algorithm is that the distance metrics are builded on image feature's interior manifold structure, and the dimensionality reduction on manifold structure can handle the high dimensionality challenge faced by KML. Final experiments verify our method's efficiency and effectiveness by comparing it with state-of-the-art image annotation approaches.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.7 pp.1396-1400
- Publication Date
- 2015/07/01
- Publicized
- 2015/04/03
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDL8216
- Type of Manuscript
- LETTER
- Category
- Pattern Recognition
Authors
Lihua GUO
South China University of Technology
Keyword
Latest Issue
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Lihua GUO, "Manifold Kernel Metric Learning for Larger-Scale Image Annotation" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 7, pp. 1396-1400, July 2015, doi: 10.1587/transinf.2014EDL8216.
Abstract: An appropriate similarity measure between images is one of the key techniques in search-based image annotation models. In order to capture the nonlinear relationships between visual features and image semantics, many kernel distance metric learning(KML) algorithms have been developed. However, when challenged with large-scale image annotation, their metrics can't explicitly represent the similarity between image semantics, and their algorithms suffer from high computation cost. Therefore, they always lose their efficiency. In this paper, we propose a manifold kernel metric learning (M_KML) algorithm. Our M_KML algorithm will simultaneously learn the manifold structure and the image annotation metrics. The main merit of our M_KML algorithm is that the distance metrics are builded on image feature's interior manifold structure, and the dimensionality reduction on manifold structure can handle the high dimensionality challenge faced by KML. Final experiments verify our method's efficiency and effectiveness by comparing it with state-of-the-art image annotation approaches.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDL8216/_p
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@ARTICLE{e98-d_7_1396,
author={Lihua GUO, },
journal={IEICE TRANSACTIONS on Information},
title={Manifold Kernel Metric Learning for Larger-Scale Image Annotation},
year={2015},
volume={E98-D},
number={7},
pages={1396-1400},
abstract={An appropriate similarity measure between images is one of the key techniques in search-based image annotation models. In order to capture the nonlinear relationships between visual features and image semantics, many kernel distance metric learning(KML) algorithms have been developed. However, when challenged with large-scale image annotation, their metrics can't explicitly represent the similarity between image semantics, and their algorithms suffer from high computation cost. Therefore, they always lose their efficiency. In this paper, we propose a manifold kernel metric learning (M_KML) algorithm. Our M_KML algorithm will simultaneously learn the manifold structure and the image annotation metrics. The main merit of our M_KML algorithm is that the distance metrics are builded on image feature's interior manifold structure, and the dimensionality reduction on manifold structure can handle the high dimensionality challenge faced by KML. Final experiments verify our method's efficiency and effectiveness by comparing it with state-of-the-art image annotation approaches.},
keywords={},
doi={10.1587/transinf.2014EDL8216},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Manifold Kernel Metric Learning for Larger-Scale Image Annotation
T2 - IEICE TRANSACTIONS on Information
SP - 1396
EP - 1400
AU - Lihua GUO
PY - 2015
DO - 10.1587/transinf.2014EDL8216
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2015
AB - An appropriate similarity measure between images is one of the key techniques in search-based image annotation models. In order to capture the nonlinear relationships between visual features and image semantics, many kernel distance metric learning(KML) algorithms have been developed. However, when challenged with large-scale image annotation, their metrics can't explicitly represent the similarity between image semantics, and their algorithms suffer from high computation cost. Therefore, they always lose their efficiency. In this paper, we propose a manifold kernel metric learning (M_KML) algorithm. Our M_KML algorithm will simultaneously learn the manifold structure and the image annotation metrics. The main merit of our M_KML algorithm is that the distance metrics are builded on image feature's interior manifold structure, and the dimensionality reduction on manifold structure can handle the high dimensionality challenge faced by KML. Final experiments verify our method's efficiency and effectiveness by comparing it with state-of-the-art image annotation approaches.
ER -
English
