IEICE TRANSACTIONS on Information
Visualizing Web Images Using Fisher Discriminant Locality Preserving Canonical Correlation Analysis
Summary :
A novel dimensionality reduction method, Fisher Discriminant Locality Preserving Canonical Correlation Analysis (FDLP-CCA), for visualizing Web images is presented in this paper. FDLP-CCA can integrate two modalities and discriminate target items in terms of their semantics by considering unique characteristics of the two modalities. In this paper, we focus on Web images with text uploaded on Social Networking Services for these two modalities. Specifically, text features have high discriminate power in terms of semantics. On the other hand, visual features of images give their perceptual relationships. In order to consider both of the above unique characteristics of these two modalities, FDLP-CCA estimates the correlation between the text and visual features with consideration of the cluster structure based on the text features and the local structures based on the visual features. Thus, FDLP-CCA can integrate the different modalities and provide separated manifolds to organize enhanced compactness within each natural cluster.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.9 pp.2005-2016
- Publication Date
- 2017/09/01
- Publicized
- 2017/06/14
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2016PCP0005
- Type of Manuscript
- Special Section PAPER (Special Section on Picture Coding and Image Media Processing)
- Category
Authors
Kohei TATENO
Hokkaido University
Takahiro OGAWA
Hokkaido University
Miki HASEYAMA
Hokkaido University
Keyword
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Kohei TATENO, Takahiro OGAWA, Miki HASEYAMA, "Visualizing Web Images Using Fisher Discriminant Locality Preserving Canonical Correlation Analysis" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 9, pp. 2005-2016, September 2017, doi: 10.1587/transinf.2016PCP0005.
Abstract: A novel dimensionality reduction method, Fisher Discriminant Locality Preserving Canonical Correlation Analysis (FDLP-CCA), for visualizing Web images is presented in this paper. FDLP-CCA can integrate two modalities and discriminate target items in terms of their semantics by considering unique characteristics of the two modalities. In this paper, we focus on Web images with text uploaded on Social Networking Services for these two modalities. Specifically, text features have high discriminate power in terms of semantics. On the other hand, visual features of images give their perceptual relationships. In order to consider both of the above unique characteristics of these two modalities, FDLP-CCA estimates the correlation between the text and visual features with consideration of the cluster structure based on the text features and the local structures based on the visual features. Thus, FDLP-CCA can integrate the different modalities and provide separated manifolds to organize enhanced compactness within each natural cluster.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016PCP0005/_p
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@ARTICLE{e100-d_9_2005,
author={Kohei TATENO, Takahiro OGAWA, Miki HASEYAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Visualizing Web Images Using Fisher Discriminant Locality Preserving Canonical Correlation Analysis},
year={2017},
volume={E100-D},
number={9},
pages={2005-2016},
abstract={A novel dimensionality reduction method, Fisher Discriminant Locality Preserving Canonical Correlation Analysis (FDLP-CCA), for visualizing Web images is presented in this paper. FDLP-CCA can integrate two modalities and discriminate target items in terms of their semantics by considering unique characteristics of the two modalities. In this paper, we focus on Web images with text uploaded on Social Networking Services for these two modalities. Specifically, text features have high discriminate power in terms of semantics. On the other hand, visual features of images give their perceptual relationships. In order to consider both of the above unique characteristics of these two modalities, FDLP-CCA estimates the correlation between the text and visual features with consideration of the cluster structure based on the text features and the local structures based on the visual features. Thus, FDLP-CCA can integrate the different modalities and provide separated manifolds to organize enhanced compactness within each natural cluster.},
keywords={},
doi={10.1587/transinf.2016PCP0005},
ISSN={1745-1361},
month={September},}
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TY - JOUR
TI - Visualizing Web Images Using Fisher Discriminant Locality Preserving Canonical Correlation Analysis
T2 - IEICE TRANSACTIONS on Information
SP - 2005
EP - 2016
AU - Kohei TATENO
AU - Takahiro OGAWA
AU - Miki HASEYAMA
PY - 2017
DO - 10.1587/transinf.2016PCP0005
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2017
AB - A novel dimensionality reduction method, Fisher Discriminant Locality Preserving Canonical Correlation Analysis (FDLP-CCA), for visualizing Web images is presented in this paper. FDLP-CCA can integrate two modalities and discriminate target items in terms of their semantics by considering unique characteristics of the two modalities. In this paper, we focus on Web images with text uploaded on Social Networking Services for these two modalities. Specifically, text features have high discriminate power in terms of semantics. On the other hand, visual features of images give their perceptual relationships. In order to consider both of the above unique characteristics of these two modalities, FDLP-CCA estimates the correlation between the text and visual features with consideration of the cluster structure based on the text features and the local structures based on the visual features. Thus, FDLP-CCA can integrate the different modalities and provide separated manifolds to organize enhanced compactness within each natural cluster.
ER -
English
