Optimized Method of Multi-Feature for Content-based Image Retrieval

DOI

10.2991/icmii-15.2015.151How to use a DOI?

Keywords

multi-feature, hierarchy clustering, image-retrieval

Abstract

Content-based image retrieval has been focused on attention during recent years. Traditional methods of CBIR most relied on single feature extraction of images, which could only reflect single characters of the image. We proposed an optimized structure of multi-feature for content-based image retrieval. We extracted the color feature using HSV bin to reflect the holistic feature of the image, and then extract the sift descriptor to produce Bag-of-Words bin to reflect the local feature of the image. Using those two features to get fusion vectors represents the image synthetically. Finally, we use hierarchy clustering to cluster images in the database to get efficient retrieval results. In the experiment, we test the precision of our method comparing with the same type of feature extraction combining method to validate our promotion.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhengyan Dai
AU  - Sujuan Qin
PY  - 2015/10
DA  - 2015/10
TI  - Optimized Method of Multi-Feature for Content-based Image Retrieval
BT  - Proceedings of the 3rd International Conference on Mechatronics and Industrial Informatics
PB  - Atlantis Press
SP  - 864
EP  - 869
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmii-15.2015.151
DO  - 10.2991/icmii-15.2015.151
ID  - Dai2015/10
ER  -