D. Suganya, S. Saranya, N. Kannan, Jayaram College of Engineering and Technology, Tamilnadu, India.
DOI : 01.0401/ijaict.2014.01.32
International Journal of Advanced Information and Communication Technology
Received On : September 16, 2014
Revised On : October 25, 2014
Accepted On : November 17, 2014
Published On : December 05, 2014
Volume 01, Issue 08
Pages : 163-167
Abstract
Social networks have become popular due to its photo sharing facilities. People are interested to explore contents that contain images. Since internet has become a part of life people are interested in uploading images in it. Hence with the exponentially growing photos, large-scale content-based face image retrieval is a facilitating technology for many emerging applications. In this paper, our aim is to utilize automatically detected human attributes which contain semantic cues of the face photos to improve content based face retrieval by constructing semantic cues for efficient image retrieval. This technique is coupled with relevance ranking technique to enhance the efficiency further. Two orthogonalmethods named attribute-enhanced sparse coding and attributeembedded inverted indexing are proposed to improve the imageretrieval in both offline and online stages. Relevance ranking when added with these methods show performance improvement to greater extent.
Keywords
Face Image, Human Attributes, Content-Based Image Retrieval, Relevance Ranking.
Cite this article
D. Suganya, S. Saranya, N. Kannan, “Attribute-Enhanced Sparse Codeword’s and Inverted Indexing for Scalable Face Image Retrieval” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp.163-167, December 05, 2014.
Copyright
© 2014 D. Suganya, S. Saranya, N. Kannan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.