Robust Image Hashing Based on Content Structure Diagram
Received date: 2016-04-26
Revised date: 2016-07-07
Online published: 2016-11-30
Robust image hashing is proposed for improving robustness and matching efficiency of copy detection system. Gabor transform coefficients of the image are used to construct structure diagrams. They are transformed from the Cartesian coordinates to polar coordinates, and normalized to obtain new diagrams. The weighted sum of their subblock pixels are calculated to obtain feature vectors, which are then quantized to produce the final binary hash codes. The structure diagrams based on Gabor coefficients proposed in this work are robust and distinctive. The distorted hash code fusion and double keys used in quantization further improve robustness, distinctiveness and compactness of the algorithm. The proposed method is compared with several representative image hashing approaches such as the non-negative matrix factorization hashing, radial and angular shape context hashing, ring partition and invariant vector distance hashing using a large image database. The results show that the overall performance of the proposed algorithm is significantly better than the other methods in terms of precision and recall rates, as well as matching efficiency.
LI Xin-wei, XIA Xiu-zhen . Robust Image Hashing Based on Content Structure Diagram[J]. Journal of Applied Sciences, 2016 , 34(6) : 691 -701 . DOI: 10.3969/j.issn.0255-8297.2016.06.005
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