Large-scale Image Retrieval based on the Vocabulary Tree

Bo Cheng, Li Zhuo, Pei Zhang, Jing Zhang

Abstract

In this paper, vocabulary tree based large-scale image retrieval scheme is proposed that can achieve higher accuracy and speed. The novelty of this paper can be summarized as follows. First, because traditional Scale Invariant Feature Transform (SIFT) descriptors are excessively concentrated in some areas of images, the extraction process of SIFT features is optimized to reduce the number. Then, combined with optimized-SIFT, color histogram in Hue, Saturation, Value (HSV) color space is extracted to be another image feature. Moreover, Local Fisher Discriminant Analysis (LFDA) is applied to reduce the dimension of SIFT and color features, which will help to shorten feature-clustering time. Finally, dimension-reduced features are used to generate vocabulary trees which will be used for large-scale image retrieval. The experimental results on several image datasets show that, the proposed method can achieve satisfying retrieval precision.

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Paper Citation


in Harvard Style

Cheng B., Zhuo L., Zhang P. and Zhang J. (2014). Large-scale Image Retrieval based on the Vocabulary Tree . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 299-304. DOI: 10.5220/0004661802990304


in Bibtex Style

@conference{visapp14,
author={Bo Cheng and Li Zhuo and Pei Zhang and Jing Zhang},
title={Large-scale Image Retrieval based on the Vocabulary Tree},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={299-304},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004661802990304},
isbn={978-989-758-004-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)
TI - Large-scale Image Retrieval based on the Vocabulary Tree
SN - 978-989-758-004-8
AU - Cheng B.
AU - Zhuo L.
AU - Zhang P.
AU - Zhang J.
PY - 2014
SP - 299
EP - 304
DO - 10.5220/0004661802990304