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An Extensive Analysis on Optimized 3D Watermarking Using Enhanced clustering Techniques

IEEE ACCESS 2024年 12卷 158966-158983页

作者： Sharine, G. Julie Anbarasi, L. Jani Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 India

The rapid advancement of technology and increased internet usage have significantly contributed to the development of multimedia technologies, such as computer games and computer graphics. A key aspect of these advancements is the creation and widespread use of 3D models. However, this rapid development has also led to issues like illegal usage and theft of 3D models. Ensuring ownership protection is crucial to addressing these challenges. An effective method for copyright protection and preserving the integrity of 3D mesh models is digital watermarking. 3D watermarking is essential for safeguarding the copyright of 3D mesh models. This proposed study introduces an innovative approach that combines three powerful techniques. Fuzzy C-Means (FCM), Gaussian Mixture Model (GMM), and K-Means clustering to cluster similar vertices and embed watermarks. These techniques are chosen due to their simplicity, efficiency and accuracy that are proven in other domains. In the proposed work the cluster size is varied for each algorithm, and the watermark is embedded in the largest cluster, specifically in the 'Z' coordinate of the model. To test the robustness of the watermark, common attacks and geometric attacks are applied to the watermarked models. The embedded watermark is extracted with an NCC value close to 1 for all clustering algorithms, indicating high accuracy. The study evaluates the imperceptibility and robustness of the watermarking method, achieving high Peak Signal To Noise Ratio (PSNR) values and low Root Mean Square Error (RMSE), which demonstrate the effectiveness of the novel approach. The results indicate that the method attains excellent Normalized Correlation Coefficient (NCC) values, confirming its reliability and robustness.

关键词： Watermarking Three-dimensional displays Solid modeling Robustness clustering algorithms Faces Data mining Computational modeling Classification algorithms Biological system modeling 3D model watermarking FCM GMM K-Means robustness imperceptibility

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On the Consistency and Large-Scale Extension of Multiple Kernel clustering

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第10期46卷 6935-6947页

作者： Liang, Weixuan Tang, Chang Liu, Xinwang Liu, Yong Liu, Jiyuan Zhu, En He, Kunlun Natl Univ Def Technol Coll Comp Changsha 410073 Hunan Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430079 Hubei Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Beijing 100872 Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Big Data Res Ctr Beijing 100010 Peoples R China

Existing multiple kernel clustering (MKC) algorithms have two ubiquitous problems. From the theoretical perspective, most MKC algorithms lack sufficient theoretical analysis, especially the consistency of learned parameters, such as the kernel weights. From the practical perspective, the high complexity makes MKC unable to handle large-scale datasets. This paper tries to address the above two issues. We first make a consistency analysis of an influential MKC method named Simple Multiple Kernel k -Means (SimpleMKKM). Specifically, suppose that gamma<^>(n) are the kernel weights learned by SimpleMKKM from the training samples. We also define the expected version of SimpleMKKM and denote its solution as gamma(& lowast;). We establish an upper bound of parallel to gamma<^>(n)-gamma(& lowast;)parallel to(infinity) in the order of O(1/root n) , where n is the sample number. Based on this result, we also derive its excess clustering risk calculated by a standard clustering loss function. For the large-scale extension, we replace the eigen decomposition of SimpleMKKM with singular value decomposition (SVD). Consequently, the complexity can be decreased to O(n) such that SimpleMKKM can be implemented on large-scale datasets. We then deduce several theoretical results to verify the approximation ability of the proposed SVD-based method. The results of comprehensive experiments demonstrate the superiority of the proposed method.

关键词： Kernel clustering algorithms Training Matrix decomposition Upper bound Standards Eigenvalues and eigenfunctions Multiple kernel clustering consistency large-scale extension

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K-Means clustering With Natural Density Peaks for Discovering Arbitrary-Shaped Clusters

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第8期35卷 11077-11090页

作者： Cheng, Dongdong Huang, Jinlong Zhang, Sulan Xia, Shuyin Wang, Guoyin Xie, Jiang Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China Yangtze Normal Univ Coll Big Data & Intelligent Engn Chongqing 408100 Peoples R China

Due to simplicity, K-means has become a widely used clustering method. However, its clustering result is seriously affected by the initial centers and the allocation strategy makes it hard to identify manifold clusters. Many improved K-means are proposed to accelerate it and improve the quality of initialize cluster centers, but few researchers pay attention to the shortcoming of K-means in discovering arbitrary-shaped clusters. Using graph distance (GD) to measure the dissimilarity between objects is a good way to solve this problem, but computing the GD is time-consuming. Inspired by the idea that granular ball uses a ball to represent the local data, we select representatives from a local neighborhood, called natural density peaks (NDPs). On the basis of NDPs, we propose a novel K-means algorithm for identifying arbitrary-shaped clusters, called NDP-Kmeans. It defines neighbor-based distance between NDPs and takes advantage of the neighbor-based distance to compute the GD between NDPs. Afterward, an improved K-means with high-quality initial centers and GD is used to cluster NDPs. Finally, each remaining object is assigned according to its representative. The experimental results show that our algorithms can not only recognize spherical clusters but also manifold clusters. Therefore, NDP-Kmeans has more advantages in detecting arbitrary-shaped clusters than other excellent algorithms.

关键词： clustering algorithms Manifolds Kernel Approximation algorithms Object recognition Resource management Partitioning algorithms Granular ball graph distance (GD) K-means natural density peaks (NDPs)

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Dual Consensus Anchor Learning for Fast Multi-View clustering

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2024年 33卷 5298-5311页

作者： Qin, Yalan Qin, Chuan Zhang, Xinpeng Feng, Guorui Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China

Multi-view clustering usually attempts to improve the final performance by integrating graph structure information from different views and methods based on anchor are presented to reduce the computation cost for datasets with large scales. Despite significant progress, these methods pay few attentions to ensuring that the cluster structure correspondence between anchor graph and partition is built on multi-view datasets. Besides, they ignore to discover the anchor graph depicting the shared cluster assignment across views under the orthogonal constraint on actual bases in factorization. In this paper, we propose a novel Dual consensus Anchor Learning for Fast multi-view clustering (DALF) method, where the cluster structure correspondence between anchor graph and partition is guaranteed on multi-view datasets with large scales. It jointly learns anchors, constructs anchor graph and performs partition under a unified framework with the rank constraint imposed on the built Laplacian graph and the orthogonal constraint on the centroid representation. DALF simultaneously focuses on the cluster structure in the anchor graph and partition. The final cluster structure is simultaneously shown in the anchor graph and partition. We introduce the orthogonal constraint on the centroid representation in anchor graph factorization and the cluster assignment is directly constructed, where the cluster structure is shown in the partition. We present an iterative algorithm for solving the formulated problem. Extensive experiments demonstrate the effectiveness and efficiency of DALF on different multi-view datasets compared with other methods.

关键词： Matrix decomposition clustering methods clustering algorithms Partitioning algorithms Laplace equations Periodic structures Optimization Multi-view clustering unified framework cluster structure rank constraints anchors efficiency

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Adaptive Markov clustering Algorithm for Optimal Event Detection From Heterogeneous News Documents

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IEEE ACCESS 2024年 12卷 72079-72097页

作者： Al-Dyani, Wafa Zubair Hadhramout Univ Coll Comp & Informat Technol Dept Comp Sci Hadhramout Yemen

Several Event Detection (ED) applications utilize various wrapper Feature Selection (FS) techniques based on wrapping the Markov clustering Algorithm (MCL) with the Binary Bat Algorithm (BBA) or Adaptive Bat Algorithm (ABBA). These approaches have shown promising results in identifying relevant feature subset for MCL, leading to more precise event cluster from heterogeneous news articles. However, such wrapped FS methods involve coupling two methods (FS and ED) within ED model, with their performance influencing each other. While ABBA improved upon BBA's limitations, MCL's rapid convergence can hinder detection effectiveness. This fast convergence can lead to local optima and the detection of meaningless clusters. Additionally, MCL's identification ability diminishes as the feature space grows. To address these issues, this paper develops two novel adaptive techniques to control MCL's inflation (inf) and pruning (p) parameters, thereby managing its convergence behavior. Consequently, a new variant called Adaptive MCL (AMCL) is introduced and combined with ABBA. The effectiveness of the ABBA-AMCL method is evaluated using 10 benchmark datasets and two substantial Facebook news datasets. Various performance measures are employed to compare ABBA-AMCL against established methods. The empirical results demonstrate that ABBA-AMCL excels at extracting high-quality, real-world event clusters from various news text sources.

关键词： clustering algorithms Convergence Tuning Event detection Data models Planning Feature extraction Markov processes Heterogeneous networks Adaptive BBA Markov clustering (MCL) event detection (ED) methods wrapper methods heterogeneous news adaptive techniques

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Noised Multi-Layer Networks clustering With Graph Denoising and Structure Learning

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第10期36卷 5294-5307页

作者： Wu, Wenming Zhang, Wensheng Gong, Maoguo Ma, Xiaoke Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China Xidian Univ Key Lab Smart Human Comp Interact & Wearable Techn Xian 710071 Shaanxi Peoples R China Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou 510006 Guangdong Peoples R China

Multi-layer networks treat various types of interactions at each level to model complex systems in nature and society, and clustering of them is of great significance for revealing mechanisms of systems. Vast majority of current algorithms focus on identifying the common communities in clear multi-layer networks, and few attempt has been devoted to the detection of layer-specific communities in noised ones. To address these issues, a joint learning algorithm with Graph Denoising and Structure Learning (called GDSL) for the detection of layer-specific communities in noised multi-layer networks is proposed, which simultaneously integrates graph denoising, structure learning, and module detection. To remove noise of networks, GDSL re-constructs affinity graphs for the original ones by preserving community structure. To enhance robustness and discriminative of features, GDSL explores the relations of features among various layers with the Hilbert-Schmidt Independence Criterion and structure learning. Finally, GDSL joins all these procedures with an objective function, and deduces optimization rules. The results show that GDSL not only significantly outperforms baselines but also enhances the robustness of the algorithm, providing an effective model for community detection in noised multi-layer networks.

关键词： Noise reduction clustering algorithms Representation learning Optimization Feature extraction Complex systems Topology Multi-layer networks graph denoising structure learning p norm community detection

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A Cascade Domain clustering Algorithm for Multiview DSM Fusion From Urban Satellite Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1-21页

作者： Zhao, Li Men, Yubo Zhu, Yi Wang, Haiyan Men, Chaoguang Jiangsu Normal Univ Coll Comp Sci & Technol Xuzhou 221116 Peoples R China China Acad Space Technol Space Star Technol Co Ltd Beijing 100089 Peoples R China Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China

Multiview digital surface model (DSM) fusion has emerged as an important technique for 3-D reconstruction of multiview satellite images. However, existing multiview DSM fusion approaches are prone to the problems of blurred elevation divisions at the object edges, salt and pepper noises on smooth surfaces, and severe loss of surface details in weakly textured regions. In this article, we present a cascade domain clustering (CDC) algorithm for fusing multiview DSMs, which is realized by the combination of salient domain clustering and model domain clustering. Initially, the salient domain clustering is employed to demarcate prominent objects and identify regional edges using 2-D spectral and 3-D elevation information. Subsequently, to further segment the intricate surface structures, particularly for objects with low texture attributes, we implement the model domain clustering to iteratively aggregate 3-D points and fit geometric models corresponding to the aggregated clusters. Finally, the multiview DSMs are fused iteratively through the weighted least-squares (WLS) method, with model clusters serving as the fundamental units, under the constraints of the geometric models. Sufficient experiments show that the proposed CDC algorithm surpasses other popular multiview DSM fusion algorithms in terms of completeness and accuracy, achieving 91.51% completeness and 0.93-m root-mean-square error (RMSE), representing a 79.89% improvement in completeness and an 86.40% reduction in RMSE compared to the popular stereo 3-D reconstruction pipeline.

关键词： Three-dimensional displays Satellite images Solid modeling Image edge detection clustering algorithms Shape Satellites Cascade domain clustering (CDC) geometric model multiview digital surface model (DSM) urban satellite image

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DeepCluE: Enhanced Deep clustering via Multi-Layer Ensembles in Neural Networks

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第2期8卷 1582-1594页

作者： Huang, Dong Chen, Ding-Hua Chen, Xiangji Wang, Chang-Dong Lai, Jian-Huang South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China Minist Agr & Rural Affairs Key Lab Smart Agr Technol Trop South China Guangzhou 510642 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Guangdong Prov Key Lab Intellectual Property & Big Guangzhou 510665 Peoples R China Guangdong Key Lab Informat Secur Technol Guangzhou 510006 Peoples R China

Deep clustering has recently emerged as a promising technique for complex data clustering. Despite the considerable progress, previous deep clustering works mostly build or learn the final clustering by only utilizing a single layer of representation, e.g., by performing the K-means clustering on the last fully-connected layer or by associating some clustering loss to a specific layer, which neglect the possibilities of jointly leveraging multi-layer representations for enhancing the deep clustering performance. In view of this, this paper presents a Deep clustering via Ensembles (DeepCluE) approach, which bridges the gap between deep clustering and ensemble clustering by harnessing the power of multiple layers in deep neural networks. In particular, we utilize a weight-sharing convolutional neural network as the backbone, which is trained with both the instance-level contrastive learning (via an instance projector) and the cluster-level contrastive learning (via a cluster projector) in an unsupervised manner. Thereafter, multiple layers of feature representations are extracted from the trained network, upon which an off-the-shelf ensemble clustering process is conducted. Specifically, a set of diversified base clusterings are generated from the multi-layer representations via a highly efficient clusterer. Then the reliability of clusters in multiple base clusterings is automatically estimated by exploiting an entropy-based criterion, based on which the set of base clusterings are further formulated into a weighted-cluster bipartite graph. By partitioning this bipartite graph via transfer cut, the final consensus clustering can be obtained. Experimental results on six image datasets confirm the advantages of DeepCluE over the state-of-the-art deep clustering approaches.

关键词： Self-supervised learning Feature extraction clustering methods Computational intelligence Data augmentation clustering algorithms Training Deep clustering ensemble clustering image clustering deep neural network contrastive learning

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Deep clustering With Hybrid-Grained Contrastive and Discriminative Learning

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第10期34卷 9472-9483页

作者： Huang, Dong Deng, Xiaozhi Chen, Ding-Hua Wen, Zihao Sun, Weijun Wang, Chang-Dong Lai, Jian-Huang South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China

Deep contrastive clustering has recently gained significant attention due to its advantageous ability to leverage the contrastive learning paradigm for joint representation learning and clustering. However, previous deep contrastive clustering approaches mostly focus on instance discrimination or cluster discrimination, which often overlook the rich semantic information latent in the vast intermediate levels of granularity between instances and clusters. Moreover, they are typically prone to utilizing relationships only within the same level of granularity, e.g., instance-instance relationships and cluster-cluster relationships, but frequently neglect the interactions between different granularity-levels. To tackle these issues, this paper presents a novel end-to-end deep contrastive clustering approach termed Deep clustering with Hybrid-Grained Contrastive and Discriminative Learning (DCHL). Particularly, the instance-level contrastive learning and cluster-level contrastive learning are first formulated, where the cluster-level contrastive learning is further split into fine-grained and coarse-grained branches. To capture global dependencies, the cluster-level contrastiveness is explored on the coarse-grained cluster branch. Meanwhile, to capture hybrid-grained relationships, the dual-level instance-group discrimination learning is enforced between the instance branch and the fine-grained cluster branch, where the self instance-group discrimination and the cross instance-group discrimination are simultaneously optimized for enhancing the deep clustering performance. Experiments on five challenging image datasets confirm the superiority of DCHL over the state-of-the-art. Code available: https://***/dengxiaozhi/DCHL.

关键词： Feature extraction Representation learning Semantics clustering methods clustering algorithms Prototypes Network architecture Data clustering image clustering deep clustering contrastive clustering granularity

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An Overview of Advanced Deep Graph Node clustering

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第1期11卷 1302-1314页

作者： Wang, Shiping Yang, Jinbin Yao, Jie Bai, Yang Zhu, William Fuzhou Univ Coll Comp & Data Sci Fujian Prov Key Lab Network Comp & Intelligent Inf Fuzhou Peoples R China Chengdu Univ Informat Technol Sch Cyberspace Secur Chengdu Peoples R China Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu Peoples R China

Graph data have become increasingly important, and graph node clustering has emerged as a fundamental task in data analysis. In recent years, graph node clustering has gradually moved from traditional shallow methods to deep neural networks due to the powerful representation capabilities of deep learning. In this article, we review some representatives of the latest graph node clustering methods, which are classified into three categories depending on their principles. Extensive experiments are conducted on real-world graph datasets to evaluate the performance of these methods. Four mainstream evaluation performance metrics are used, including clustering accuracy, normalized mutual information, adjusted rand index, and F1-score. Based on the experimental results, several potential research challenges and directions in the field of deep graph node clustering are pointed out. This work is expected to facilitate researchers interested in this field to provide some insights and further promote the development of deep graph node clustering.

关键词： clustering methods clustering algorithms Task analysis Deep learning Neural networks Feature extraction Computational modeling Deep clustering deep learning graph node clustering neural networks unsupervised learning

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