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  • Open Access

    ARTICLE

    IDSH: An Improved Deep Supervised Hashing Method for Image Retrieval

    Chaowen Lu1,a, Feifei Lee1,a,*, Lei Chen1, Sheng Huang1, Qiu Chen2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.2, pp. 593-608, 2019, DOI:10.32604/cmes.2019.07796

    Abstract Image retrieval has become more and more important because of the explosive growth of images on the Internet. Traditional image retrieval methods have limited image retrieval performance due to the poor image expression abhility of visual feature and high dimension of feature. Hashing is a widely-used method for Approximate Nearest Neighbor (ANN) search due to its rapidity and timeliness. Meanwhile, Convolutional Neural Networks (CNNs) have strong discriminative characteristics which are used for image classification. In this paper, we propose a CNN architecture based on improved deep supervised hashing (IDSH) method, by which the binary compact codes can be generated directly.… More >

  • Open Access

    ARTICLE

    Balanced Deep Supervised Hashing

    Hefei Ling1, Yang Fang1, Lei Wu1, Ping Li1,*, Jiazhong Chen1, Fuhao Zou1, Jialie Shen2

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 85-100, 2019, DOI:10.32604/cmc.2019.05588

    Abstract Recently, Convolutional Neural Network (CNN) based hashing method has achieved its promising performance for image retrieval task. However, tackling the discrepancy between quantization error minimization and discriminability maximization of network outputs simultaneously still remains unsolved. Motivated by the concern, we propose a novel Balanced Deep Supervised Hashing (BDSH) based on variant posterior probability to learn compact discriminability-preserving binary code for large scale image data. Distinguished from the previous works, BDSH can search an equilibrium point within the discrepancy. Towards the goal, a delicate objective function is utilized to maximize the discriminability of the output space with the variant posterior probability… More >

  • Open Access

    ARTICLE

    Fast Near-duplicate Image Detection in Riemannian Space by A Novel Hashing Scheme

    Ligang Zheng1,*, Chao Song2

    CMC-Computers, Materials & Continua, Vol.56, No.3, pp. 529-539, 2018, DOI: 10.3970/cmc.2018.03780

    Abstract There is a steep increase in data encoded as symmetric positive definite (SPD) matrix in the past decade. The set of SPD matrices forms a Riemannian manifold that constitutes a half convex cone in the vector space of matrices, which we sometimes call SPD manifold. One of the fundamental problems in the application of SPD manifold is to find the nearest neighbor of a queried SPD matrix. Hashing is a popular method that can be used for the nearest neighbor search. However, hashing cannot be directly applied to SPD manifold due to its non-Euclidean intrinsic geometry. Inspired by the idea… More >

  • Open Access

    ARTICLE

    Robust Image Hashing via Random Gabor Filtering and DWT

    Zhenjun Tang1,*, Man Ling1, Heng Yao1, Zhenxing Qian2, Xianquan Zhang1, Jilian Zhang3, Shijie Xu1

    CMC-Computers, Materials & Continua, Vol.55, No.2, pp. 331-344, 2018, DOI:10.3970/cmc.2018.02222

    Abstract Image hashing is a useful multimedia technology for many applications, such as image authentication, image retrieval, image copy detection and image forensics. In this paper, we propose a robust image hashing based on random Gabor filtering and discrete wavelet transform (DWT). Specifically, robust and secure image features are first extracted from the normalized image by Gabor filtering and a chaotic map called Skew tent map, and then are compressed via a single-level 2-D DWT. Image hash is finally obtained by concatenating DWT coefficients in the LL sub-band. Many experiments with open image datasets are carried out and the results illustrate… More >

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