Joint Learning of Self-Representation and Indicator for Multi-View Image Clustering
Nanjing University of Posts and Telecommunications（南京邮电大学）；University of Trento（特伦托大学）；Texas State University（得克萨斯州立大学）；Wuhan University（武汉大学）；Minjiang University（闽江学院）
Songsong Wu, Zhiqiang Lu, Hao Tang, Yan Yan, Songhao Zhu, Xiao-Yuan Jing, Zuoyong Li
Multi-view subspace clustering aims to divide a set of multisource data into several groups according to their underlying subspace structure. Although the spectral clustering based methods achieve promotion in multi-view clustering, their utility is limited by the separate learning manner in which affinity matrix construction and cluster indicator estimation are isolated. In this paper, we propose to jointly learn the self-representation, continue and discrete cluster indicators in an unified model. Our model can explore the subspace structure of each view and fusion them to facilitate clustering simultaneously. Experimental results on two benchmark datasets demonstrate that our method outperforms other existing competitive multi-view clustering methods.
Chongqing University（重庆大学）；Rice University（莱斯大学）；University of Electronic Science and Technology of China（电子科技大学）；The Chinese University of Hong Kong-Shenzhen（香港中文大学（深圳））
The K-means algorithm is arguably the most popular data clustering method, commonly applied to processed datasets in some "feature spaces", as is in spectral clustering. Highly sensitive to initializations, however, K-means encounters a scalability bottleneck with respect to the number of clusters K as this number grows in big data applications. In this work, we promote a closely related model called K-indicators model and construct an efficient, semi-convex-relaxation algorithm that requires no randomized initializations. We present extensive empirical results to show advantages of the new al…
Xiamen University（厦门大学）；Chinese Academy of Sciences（中国科学院）；Sun Yat-sen University（中山大学）
In this paper, we propose a novel Joint framework for Deep Multi-view Clustering (DMJC), where multiple deep embedded features, multi-view fusion mechanism and clustering assignments can be learned simultaneously. Our key idea is that the joint learning strategy can sufficiently exploit clustering-friendly multi-view features and useful multi-view complementary information to improve the clustering performance. How to realize the multi-view fusion in such a joint framework is the primary challenge. To do so, we design two ingenious variants of deep multi-view joint clustering models under the …
One of the most promising approaches for unsupervised learning is combining deep representation learning and deep clustering. Some recent works propose to simultaneously learn representation using deep neural networks and perform clustering by defining a clustering loss on top of embedded features. However, these approaches are sensitive to imbalanced data and out-of-distribution samples. As a consequence, these methods optimize clustering by pushing data close to randomly initialized cluster centers. This is problematic when the number of instances varies largely in different classes or a clu…
Joint Superpixel and Self-Representation Learning for Scalable Hyperspectral Image Clustering
Department of Telecommunications and Information Processing（电信与信息处理系）；Ghent University（根特大学）；Royal Institute for Cultural Heritage (KIK-IRPA)（皇家文化遗产研究院）；School of Computer Science（计算机科学学院）；China University of Geosciences（中国地质大学）
Beijing University of Posts and Telecommunications（北京邮电大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Subspace clustering refers to the problem of segmenting data drawn from a union of subspaces. State-of-the-art approaches for solving this problem follow a two-stage approach. In the first step, an affinity matrix is learned from the data using sparse or low-rank minimization techniques. In the second step, the segmentation is found by applying spectral clustering to this affinity. While this approach has led to state-of-the-art results in many applications, it is sub-optimal because it does not exploit the fact that the affinity and the segmentation depend on each other. In this paper, we pro…
Laboratoire Hubert Curien, Université Jean Monnet（让·莫奈大学于贝尔·居里实验室）
Recent advances in depth imaging sensors provide easy access to the synchronized depth with color, called RGB-D image. In this paper, we propose an unsupervised method for indoor RGB-D image segmentation and analysis. We consider a statistical image generation model based on the color and geometry of the scene. Our method consists of a joint color-spatial-directional clustering method followed by a statistical planar region merging method. We evaluate our method on the NYU depth database and compare it with existing unsupervised RGB-D segmentation methods. Results show that, it is comparable w…
MPI for Informatics, SIC（马克斯·普朗克信息学研究所）；Saarland University（萨尔大学）；Aalto University（阿尔托大学）；University of Bonn（波恩大学）；TU Munich（慕尼黑工业大学）
This paper proposes a new algorithm for simultaneous graph matching and clustering. For the first time in the literature, these two problems are solved jointly and synergetically without relying on any training data, which brings advantages for identifying similar arbitrary objects in compound 3D scenes and matching them. For joint reasoning, we first rephrase graph matching as a rigid point set registration problem operating on spectral graph embeddings. Consequently, we utilise efficient convex semidefinite program relaxations for aligning points in Hilbert spaces and add coupling constraint…
Contrastive learning is among the most successful methods for visual representation learning, and its performance can be further improved by jointly performing clustering on the learned representations. However, existing methods for joint clustering and contrastive learning do not perform well on long-tailed data distributions, as majority classes overwhelm and distort the loss of minority classes, thus preventing meaningful representations to be learned. Motivated by this, we develop a novel joint clustering and contrastive learning framework by adapting the debiased contrastive loss to avoid…
Nanjing University of Science and Technology（南京理工大学）
Clustering analysis, a classical issue in data mining, is widely used in various research areas. This article aims at proposing a self-adaption grey DBSCAN clustering (SAG-DBSCAN) algorithm. First, the grey relational matrix is used to obtain the grey local density indicator, and then this indicator is applied to make self-adapting noise identification for obtaining a dense subset of clustering dataset, finally, the DBSCAN which automatically selects parameters is utilized to cluster the dense subset. Several frequently-used datasets were used to demonstrate the performance and effectiveness o…
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；German Centre for Neurodegenerative Diseases (DZNE)（德国神经退行性疾病中心）；Erasmus MC - University Medical Center Rotterdam（伊拉斯谟医学中心——鹿特丹大学医学中心）
We propose and demonstrate a joint model of anatomical shapes, image features and clinical indicators for statistical shape modeling and medical image analysis. The key idea is to employ a copula model to separate the joint dependency structure from the marginal distributions of variables of interest. This separation provides flexibility on the assumptions made during the modeling process. The proposed method can handle binary, discrete, ordinal and continuous variables. We demonstrate a simple and efficient way to include binary, discrete and ordinal variables into the modeling. We build Baye…
Given a large unlabeled set of images, how to efficiently and effectively group them into clusters based on extracted visual representations remains a challenging problem. To address this problem, we propose a convolutional neural network (CNN) to jointly solve clustering and representation learning in an iterative manner. In the proposed method, given an input image set, we first randomly pick k samples and extract their features as initial cluster centroids using the proposed CNN with an initial model pre-trained from the ImageNet dataset. Mini-batch k-means is then performed to assign clust…
Recently, two methods have shown outstanding performance for clustering images and jointly learning the feature representation. The first, called Information Maximiz-ing Self-Augmented Training (IMSAT), maximizes the mutual information between input and clusters while using a regularization term based on virtual adversarial examples. The second, named Invariant Information Clustering (IIC), maximizes the mutual information between the clustering of a sample and its geometrically transformed version. These methods use mutual information in distinct ways and leverage different kinds of transform…
School of Information and Communication Engineering, Dalian University of Technology（大连理工大学信息与通信工程学院）；National Laboratory of Pattern Recognition, CASIA（中国科学院自动化研究所模式识别国家重点实验室）；University of Chinese Academy of Sciences (UCAS)（中国科学院大学）
Due to the existence of various views or representations in many real-world data, multi-view learning has drawn much attention recently. Multi-view spectral clustering methods based on similarity matrixes or graphs are pretty popular. Generally, these algorithms learn informative graphs by directly utilizing original data. However, in the real-world applications, original data often contain noises and outliers that lead to unreliable graphs. In addition, different views may have different contributions to data clustering. In this paper, a novel Multiview Subspace Clustering method unifying Ada…
South China Agricultural University（华南农业大学）；Sun Yat-sen University（中山大学）
Multi-view subspace clustering aims to discover the hidden subspace structures from multiple views for robust clustering, and has been attracting considerable attention in recent years. Despite significant progress, most of the previous multi-view subspace clustering algorithms are still faced with two limitations. First, they usually focus on the consistency (or commonness) of multiple views, yet often lack the ability to capture the cross-view inconsistencies in subspace representations. Second, many of them overlook the local structures of multiple views and cannot jointly leverage multiple…
One-step Multi-view Clustering With Adaptive Low-rank Anchor-graph Learning
National Key Laboratory of Human–Machine Hybrid Augmented Intelligence（人机混合增强智能国家重点实验室）；National Engineering Research Center for Visual Information and Applications（视觉信息与应用国家工程研究中心）；Institute of Artificial Intelligence and Robotics（人工智能与机器人研究所）；Xi’an Jiaotong University（西安交通大学）；School of Electrical and Electronic Engineering（电子工程学院）；Nanyang Technological University（南洋理工大学）
Università della Svizzera italiana（瑞士意大利语区大学）；University of Hamburg（汉堡大学）；Tel Aviv University（特拉维夫大学）
We construct an extension of diffusion geometry to multiple modalities through joint approximate diagonalization of Laplacian matrices. This naturally extends classical data analysis tools based on spectral geometry, such as diffusion maps and spectral clustering. We provide several synthetic and real examples of manifold learning, retrieval, and clustering demonstrating that the joint diffusion geometry frequently better captures the inherent structure of multi-modal data. We also show that many previous attempts to construct multimodal spectral clustering can be seen as particular cases of j…
Deep learning technology has enabled successful modeling of complex facial features when high quality images are available. Nonetheless, accurate modeling and recognition of human faces in real world scenarios `on the wild' or under adverse conditions remains an open problem. When unconstrained faces are mapped into deep features, variations such as illumination, pose, occlusion, etc., can create inconsistencies in the resultant feature space. Hence, deriving conclusions based on direct associations could lead to degraded performance. This rises the requirement for a basic feature space analys…
University of California at Davis（加州大学戴维斯分校）；Huazhong University of Science and Technology（华中科技大学）；University of Bergen（卑尔根大学）；Hong Kong University of Science and Technology（香港科技大学）
We propose an effective framework for multi-phase image segmentation and semi-supervised data clustering by introducing a novel region force term into the Potts model. Assume the probability that a pixel or a data point belongs to each class is known a priori. We show that the corresponding indicator function obeys the Bernoulli distribution and the new region force function can be computed as the negative log-likelihood function under the Bernoulli distribution. We solve the Potts model by the primal-dual hybrid gradient method and the augmented Lagrangian method, which are based on two diffe…
University of Melbourne（墨尔本大学）；Monash University（莫纳什大学）；University of New South Wales（新南威尔士大学）；Northwestern Polytechnical University（西北工业大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）
Spectral clustering is an effective methodology for unsupervised learning. Most traditional spectral clustering algorithms involve a separate two-step procedure and apply the transformed new representations for the final clustering results. Recently, much progress has been made to utilize the non-negative feature property in real-world data and to jointly learn the representation and clustering results. However, to our knowledge, no previous work considers a unified model that incorporates the important multi-view information with those properties, which severely limits the performance of exis…
University of Stuttgart（斯图加特大学）；AISIA Lab, University of Science -VNU HCM（越南国立大学胡志明市理科大学AISIA实验室）；Dongguk University（东国大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Helmholtz AI, Helmholtz Munich（亥姆霍兹慕尼黑亥姆霍兹人工智能）；University of Bonn（波恩大学）；University of California San Diego（加利福尼亚大学圣迭戈分校）；Oldenburg University（奥尔登堡大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）
Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data. However, most current SSL techniques in the medical field have been designed for either 2D images or 3D volumes. In practice, this restricts the capability to fully leverage unlabeled data from numerous sources, which may include both 2D and 3D data. Additionally…
In this paper, we propose a recurrent framework for Joint Unsupervised LEarning (JULE) of deep representations and image clusters. In our framework, successive operations in a clustering algorithm are expressed as steps in a recurrent process, stacked on top of representations output by a Convolutional Neural Network (CNN). During training, image clusters and representations are updated jointly: image clustering is conducted in the forward pass, while representation learning in the backward pass. Our key idea behind this framework is that good representations are beneficial to image clustering…
Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences（中国科学院上海微系统与信息技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；ShanghaiTech University（上海科技大学）
We propose a novel 3D point cloud segmentation framework named SASO, which jointly performs semantic and instance segmentation tasks. For semantic segmentation task, inspired by the inherent correlation among objects in spatial context, we propose a Multi-scale Semantic Association (MSA) module to explore the constructive effects of the semantic context information. For instance segmentation task, different from previous works that utilize clustering only in inference procedure, we propose a Salient Point Clustering Optimization (SPCO) module to introduce a clustering procedure into the traini…
R&D department ALTECA（阿尔泰卡公司研发部门）；LIPN - CNRS UMR 7030 Sorbonne Paris Nord University（巴黎第十三大学（索邦巴黎北）法国国家科学研究中心UMR 7030实验室）；ETIS - CNRS UMR 8051 CY Cergy Paris University（CY塞尔吉-巴黎大学法国国家科学研究中心UMR 8051实验室）
East China University of Science and Technology（华东理工大学）；McMaster University（麦克马斯特大学）；Tongji University（同济大学）
Cluster assignment of large and complex images is a crucial but challenging task in pattern recognition and computer vision. In this study, we explore the possibility of employing fuzzy clustering in a deep neural network framework. Thus, we present a novel evolutionary unsupervised learning representation model with iterative optimization. It implements the deep adaptive fuzzy clustering (DAFC) strategy that learns a convolutional neural network classifier from given only unlabeled data samples. DAFC consists of a deep feature quality-verifying model and a fuzzy clustering model, where deep f…
Zhaotong University（昭通学院）；University of Macau（澳门大学）；The University of Queensland（昆士兰大学）
Multi-view clustering integrates multiple feature sets, which reveal distinct aspects of the data and provide complementary information to each other, to improve the clustering performance. It remains challenging to effectively exploit complementary information across multiple views since the original data often contain noise and are highly redundant. Moreover, most existing multi-view clustering methods only aim to explore the consistency of all views while ignoring the local structure of each view. However, it is necessary to take the local structure of each view into consideration, because …
South China Agricultural University（华南农业大学）；Sun Yat-sen University（中山大学）
Deep clustering has attracted increasing attention in recent years due to its capability of joint representation learning and clustering via deep neural networks. In its latest developments, the contrastive learning has emerged as an effective technique to substantially enhance the deep clustering performance. However, the existing contrastive learning based deep clustering algorithms mostly focus on some carefully-designed augmentations (often with limited transformations to preserve the structure), referred to as weak augmentations, but cannot go beyond the weak augmentations to explore the …
A Joint Sparse Self-Representation Learning Method for Multiview Clustering
School of Mathematics and Statistics（数学与统计学学院）；Xidian University（西安电子科技大学）；Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing（重庆非线性电路与智能信息处理重点实验室）；College of Electronic and Information Engineering（电子与信息工程学院）；Southwest University（西南大学）
This paper presents a novel framework in which video/image segmentation and localization are cast into a single optimization problem that integrates information from low level appearance cues with that of high level localization cues in a very weakly supervised manner. The proposed framework leverages two representations at different levels, exploits the spatial relationship between bounding boxes and superpixels as linear constraints and simultaneously discriminates between foreground and background at bounding box and superpixel level. Different from previous approaches that mainly rely on d…
Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；CAS Centre for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
Traditional image clustering methods take a two-step approach, feature learning and clustering, sequentially. However, recent research results demonstrated that combining the separated phases in a unified framework and training them jointly can achieve a better performance. In this paper, we first introduce fully convolutional auto-encoders for image feature learning and then propose a unified clustering framework to learn image representations and cluster centers jointly based on a fully convolutional auto-encoder and soft $k$-means scores. At initial stages of the learning procedure, the rep…
College of Economics and Management, Nanjing University of Science and Technology（南京理工大学经济管理学院）
Cluster analysis which focuses on the grouping and categorization of similar elements is widely used in various fields of research. Inspired by the phenomenon of atomic fission, a novel density-based clustering algorithm is proposed in this paper, called fission clustering (FC). It focuses on mining the dense families of a dataset and utilizes the information of the distance matrix to fissure clustering dataset into subsets. When we face the dataset which has a few points surround the dense families of clusters, K-nearest neighbors local density indicator is applied to distinguish and remove t…
University of Electronic Science and Technology of China（电子科技大学）
Multi-view clustering is an important yet challenging task due to the difficulty of integrating the information from multiple representations. Most existing multi-view clustering methods explore the heterogeneous information in the space where the data points lie. Such common practice may cause significant information loss because of unavoidable noise or inconsistency among views. Since different views admit the same cluster structure, the natural space should be all partitions. Orthogonal to existing techniques, in this paper, we propose to leverage the multi-view information by fusing partit…
City University of Macau（澳门城市大学）；National University of Defense Technology（国防科技大学）；China University of Geosciences（中国地质大学）；Zhejiang Normal University（浙江师范大学）；Research Institute of Ningbo Cixing Co. Ltd（宁波慈星股份有限公司研究院）；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Shandong Computer Science Center (National Supercomputer Center in Jinan)（山东省计算中心（国家超级计算济南中心））；Shandong Fundamental Research Center for Computer Science（山东省计算机科学基础研究中心）
University of Electronic Science and Technology of China（电子科技大学）；University of Pennsylvania（宾夕法尼亚大学）；Institute for Infocomm Research, A*Star（新加坡科技研究局资讯通信研究院）
Graph-based clustering has shown promising performance in many tasks. A key step of graph-based approach is the similarity graph construction. In general, learning graph in kernel space can enhance clustering accuracy due to the incorporation of nonlinearity. However, most existing kernel-based graph learning mechanisms is not similarity-preserving, hence leads to sub-optimal performance. To overcome this drawback, we propose a more discriminative graph learning method which can preserve the pairwise similarities between samples in an adaptive manner for the first time. Specifically, we requir…
