open access

https://hdl.handle.net/2115/70671

Visualizing Web Images Using Fisher Discriminant Locality Preserving Canonical Correlation Analysis

英語

locality preserving approach

2017-09

The Institute of Electronics, Information and Communication Engineers / IEICE

IEICE transactions on information and systems

E100D(9)

2005-2016

A novel dimensionality reduction method, Fisher Discriminant Locality Preserving Canonical Correlation Analysis (FDLPCCA), 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 onWeb 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, FDLPCCA 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.

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Copyright ©2017 The Institute of Electronics, Information and Communication Engineers

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