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The Key Extraction from Iris Features based on Wavelet Packet
Kun Yu,Juan Wei 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.7
Key extraction from the biometric is a new direction of encryption, which solves the safety problem of key a certain extent. Iris has rich textures, and its generated signature is longer than other biometric, and has unique advantages in term of key extraction. The wavelet packet is used to decompose effective iris region into the three layers. The third diagonal high frequency coefficient is extracted as the iris feature, and the encryption key was randomly generated from iris feature to meet the encryption demand. The United States NIST encryption standard is adopted to test the extracted key, and the experimental results show that through the 7 seed test of NIST, the generated key meets the security of encryption algorithm needs.
An Effective Framework for Contented-Based Image Retrieval with Multi-Instance Learning Techniques
Peng, Yu,Wei, Kun-Juan,Zhang, Da-Li The Institute of Electronics and Information Engin 2007 JUCT : Journal of Ubiquitous Convergence Technolog Vol.1 No.1
Multi-Instance Learning(MIL) performs well to deal with inherently ambiguity of images in multimedia retrieval. In this paper, an effective framework for Contented-Based Image Retrieval(CBIR) with MIL techniques is proposed, the effective mechanism is based on the image segmentation employing improved Mean Shift algorithm, and processes the segmentation results utilizing mathematical morphology, where the goal is to detect the semantic concepts contained in the query. Every sub-image detected is represented as a multiple features vector which is regarded as an instance. Each image is produced to a bag comprised of a flexible number of instances. And we apply a few number of MIL algorithms in this framework to perform the retrieval. Extensive experimental results illustrate the excellent performance in comparison with the existing methods of CBIR with MIL.
An Effective Framework for Contented-Based Image Retrieval with Multi-Instance Learning Techniques
Yu Peng,Kun-Juan Wei,Da-Li Zhang 대한전자공학회 2007 JUCT : Journal of Ubiquitous Convergence Technolog Vol.1 No.1
Multi-Instance Learning(MIL) performs well to deal with inherently ambiguity of images in multimedia retrieval. In this paper, an effective framework for Contented-Based Image Retrieval(CBIR) with MIL techniques is proposed, the effective mechanism is based on the image segmentation employing improved Mean Shift algorithm, and processes the segmentation results utilizing mathematical morphology, where the goal is to detect the semantic concepts contained in the query. Every sub-image detected is represented as a multiple features vector which is regarded as an instance. Each image is produced to a bag comprised of a flexible number of instances. And we apply a few number of MIL algorithms in this framework to perform the retrieval. Extensive experimental results illustrate the excellent performance in comparison with the existing methods of CBIR with MIL.