Loading [a11y]/accessibility-menu.js
Scalable Face Image Retrieval Using Attribute-Enhanced Sparse Codewords | IEEE Journals & Magazine | IEEE Xplore

Scalable Face Image Retrieval Using Attribute-Enhanced Sparse Codewords

Publisher: IEEE

Abstract:

Photos with people (e.g., family, friends, celebrities, etc.) are the major interest of users. Thus, with the exponentially growing photos, large-scale content-based face...View more

Abstract:

Photos with people (e.g., family, friends, celebrities, etc.) are the major interest of users. Thus, with the exponentially growing photos, large-scale content-based face image retrieval is an enabling technology for many emerging applications. In this work, we aim to utilize automatically detected human attributes that contain semantic cues of the face photos to improve content-based face retrieval by constructing semantic codewords for efficient large-scale face retrieval. By leveraging human attributes in a scalable and systematic framework, we propose two orthogonal methods named attribute-enhanced sparse coding and attribute-embedded inverted indexing to improve the face retrieval in the offline and online stages. We investigate the effectiveness of different attributes and vital factors essential for face retrieval. Experimenting on two public datasets, the results show that the proposed methods can achieve up to 43.5% relative improvement in MAP compared to the existing methods.
Published in: IEEE Transactions on Multimedia ( Volume: 15, Issue: 5, August 2013)
Page(s): 1163 - 1173
Date of Publication: 24 January 2013

ISSN Information:

Publisher: IEEE

References

References is not available for this document.