Feature Fusion based Hashing for Large Scale Image Copy Detection

DOI

10.1080/18756891.2015.1046332How to use a DOI?

Keywords

Content Based Copy Detection, Feature Fusion, Kernel Canonical Correlation Analysis, Neighborhood Structure Preserving Hashing

Abstract

Currently, researches on content based image copy detection mainly focus on robust feature extraction. However, most of existing approaches use only a single feature to represent an image for copy detection, which is often insufficient to characterize the image content. Besides, with the exponential growth of online images, it's urgent to explore a way of tackling the problem of large scale. In this paper, we propose a feature fusion based hashing method which effectively utilize the correlation between two feature models and efficiently accomplish large scale image copy detection. To accurately map images into the Hamming space, our hashing method not only preserves the local structure of individual feature but also globally consider the local structures for all the features to learn a group of hash functions. The experiment results show that the proposed method outperforms the state-of-the-art techniques in both accuracy and efficiency.

Copyright

© 2017, 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  - JOUR
AU  - Lingyu Yan
AU  - Hefei Ling
AU  - Dengpan Ye
AU  - Chunzhi Wang
AU  - Zhiwei Ye
AU  - Hongwei Chen
PY  - 2015
DA  - 2015/08/01
TI  - Feature Fusion based Hashing for Large Scale Image Copy Detection
JO  - International Journal of Computational Intelligence Systems
SP  - 725
EP  - 734
VL  - 8
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2015.1046332
DO  - 10.1080/18756891.2015.1046332
ID  - Yan2015
ER  -