An Image Hashing Scheme based on Mean-removed Vector Quantization for Multiple Purposes

Lv, Mei-Lei; Lu, Zhe‐Ming

doi:10.3923/itj.2011.120.126

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…The quantization-based algorithms have been shown to achieve superior performances [26]- [28], [19]. The representative algorithms include product quantization (PQ) [26] and Cartesian K-means (CKM) [27], which are modified versions of the conventional K-means algorithm.…”

Section: A Image Fingerprintingmentioning

confidence: 99%

Compact Image Fingerprint Via Multiple Kernel Hashing

Zou

Chen

Song

et al. 2015

IEEE Trans. Multimedia

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Image fingerprinting is regarded as an alternative approach to watermarking in terms of near-duplicate detection application. It consists of feature extraction and feature indexing. Generally, the former is mainly related to discrimination, robustness, and security while the latter closely focuses on the efficiency of fingerprints search. To enable fast fingerprints searching over a very large database, we propose a new kernelized multiple feature hashing method to convert the real-value fingerprints into compact binary-value fingerprints. During the process of converting, the proposed hashing method jointly utilizes the kernel trick and multiple feature fusion strategy to map the image represented by multiple features into a compact binary code. With the help of the kernel function, the hashing method can be applied to any format (such as string, graph, set, and so on) as long as there is an associated kernel function available for similarity measurement. In addition, taking multiple features into account aims at improving the discriminability since these multiple evidences are complementary to each other. The extensive experimental results show that the proposed algorithm outperforms state-of-the-art kernelized hashing methods by up to 10 percent.

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