提出一种基于优化网格的最小生成树聚类算法OGMST(MST Clustering Algorithm Based on Optimized Grid),一方面利用最小生成树的方法进行聚类,另一方面利用了参数自动化的网格划分技术和密度阈值处理技术,解决了最小生成树聚类算法不适用于多密度数据集的局限性,提高了现有的多密度聚类算法的效率和精度.算法还对边界点进行了有效的处理.实验表明算法具有较好的可扩展性,是一种高效、快速的聚类算法. 相似文献
Imperceptibility, robustness and data payload, which are complimentary to each other, are widely considered as the three main properties vital for any image watermarking systems. It is a challenging work to design a statistical model-based multiplicative watermarking scheme for achieving the tradeoff among three main properties. In this paper, we propose a novel statistical image watermarking scheme by modeling local redundant discrete wavelet transform (RDWT) and fast Radial harmonic Fourier moments (FRHFMs) magnitudes with bivariate Cauchy-Rayleigh distribution. Our image watermarking scheme consists of two parts, namely, embedding and detection. In the embedding process, RDWT is firstly performed on the host image and RDWT highpass subbands are divided into non-overlapping blocks. Then FRHFMs are computed on RDWT coefficient blocks. And finally, the watermark signal is inserted into robust RDWT-FRHFMs magnitudes through a non-linear multiplicative approach. In the detection process, robust local RDWT-FRHFMs magnitudes are firstly modeled by employing bivariate Cauchy-Rayleigh distribution, which can capture accurately both marginal distributions and strong dependencies of local RDWT-FRHFMs magnitudes. Statistical model parameters are then estimated effectively by the method of logarithmic cumulants (MoLC) approach. And finally, an image watermark detector for multiplicative watermarking is developed using bivariate Cauchy-Rayleigh model and locally most powerful (LMP) test. Also, we utilize the bivariate Cauchy-Rayleigh model to derive the closed-form expressions for the watermark detector. After performance testing and comparison with the experimental results of existing methods, the proposed statistical image watermarking method has achieved relatively ideal results in terms of robustness, imperceptibility and data payload.
Collaborative filtering as a classical method of information retrieval has been widely used in helping people to deal with
information overload. In this paper, we introduce the concept of local user similarity and global user similarity, based on
surprisal-based vector similarity and the application of the concept of maximin distance in graph theory. Surprisal-based
vector similarity expresses the relationship between any two users based on the quantities of information (called surprisal) contained in their ratings. Global user similarity defines two users being similar if they can be connected through their
locally similar neighbors. Based on both of Local User Similarity and Global User Similarity, we develop a collaborative filtering
framework called LS&GS. An empirical study using the MovieLens dataset shows that our proposed framework outperforms other
state-of-the-art collaborative filtering algorithms. 相似文献
In this paper, a novel watermarking scheme for quantum images based on Hadamard transform is proposed. In the new scheme, a unitary transform controlled by a classical binary key is implemented on quantum image. Then, we utilize a dynamic vector, instead of a fixed parameter as in other previous schemes, to control the embedding process. The dynamic embedding vector is decided by both the carrier quantum image and the watermark image, which is only known by the authorized owner. The proposed scheme is analyzed from visual quality, computational complexity, and payload capacity. Analysis and results show that the proposed scheme has better visual quality under a higher embedding capacity and lower complexity compared with other schemes proposed recently. 相似文献