Visualizing Web Images Using Fisher Discriminant Locality Preserving Canonical Correlation Analysis

Kohei TATENO  Takahiro OGAWA  Miki HASEYAMA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E100-D   No.9   pp.2005-2016
Publication Date: 2017/09/01
Online ISSN: 1745-1361
Type of Manuscript: Special Section PAPER (Special Section on Picture Coding and Image Media Processing)
Category: 
Keyword: 
dimensionality reduction,  visualization,  Fisher discriminant analysis,  canonical correlation analysis,  locality preserving approach,  

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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.