Abstract
Image hashing is an efficient technology for processing digital images and has been successfully used in image copy detection, image retrieval, image authentication, image quality assessment, and so on. In this paper, we design a new image hashing with compressed sensing (CS) and ordinal measures. This hashing method uses a visual attention model called Itti model and Canny operator to construct an image representation, and exploits CS to extract compact features from the representation. Finally, the CS-based compact features are quantized via ordinal measures. L2 norm is used to judge similarity of hashes produced by the proposed hashing method. Experiments about robustness validation, discrimination test, block size discussion, selection of visual attention model, selection of quantization scheme, and effectiveness of the use of ordinal measures are conducted to verify performances of the proposed hashing method. Comparisons with some state-of-the-art algorithms are also carried out. The results illustrate that the proposed hashing method outperforms some compared algorithms in classification according to ROC (receiver operating characteristic) graph.
Highlights
In the Internet era, many people publish their daily photos on the web via social platform, such as Twitter, Facebook, and Instagram
As the ordinal measure is an efficient technique for feature compression, the use of ordinal measures can derive a short hash from the compressed sensing (CS)-based compact features
We have proposed a new image hashing with CS and ordinal measures
Summary
In the Internet era, many people publish their daily photos on the web via social platform, such as Twitter, Facebook, and Instagram. (1) We exploit compressed sensing (CS) to extract compact features from the image representation constructed by visual attention model and Canny operator.
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