{"title":"DEEM: A novel approach to semi-supervised and unsupervised image clustering under uncertainty using belief functions and convolutional neural networks","authors":"Loïc Guiziou , Emmanuel Ramasso , Sébastien Thibaud , Sébastien Denneulin","doi":"10.1016/j.ijar.2025.109400","DOIUrl":null,"url":null,"abstract":"<div><div>DEEM (Deep Evidential Encoding of iMages) is a clustering algorithm that combines belief functions with convolutional neural networks in a Siamese-like framework for unsupervised and semi-supervised image clustering. In DEEM, images are mapped to Dempster–Shafer mass functions to quantify uncertainty in cluster membership. Various forms of prior information, including must-link and cannot-link constraints, supervised dissimilarities, and Distance Metric Learning, are incorporated to guide training and improve generalisation. By processing image pairs through shared network weights, DEEM aligns pairwise dissimilarities with the conflict between mass functions, thereby mitigating errors in noisy or incomplete distance matrices. Experiments on MNIST demonstrate that DEEM generalises effectively to unseen data while managing different types of prior knowledge, making it a promising approach for clustering and semi-supervised learning from image data under uncertainty.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"181 ","pages":"Article 109400"},"PeriodicalIF":3.2000,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Approximate Reasoning","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0888613X25000416","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
DEEM (Deep Evidential Encoding of iMages) is a clustering algorithm that combines belief functions with convolutional neural networks in a Siamese-like framework for unsupervised and semi-supervised image clustering. In DEEM, images are mapped to Dempster–Shafer mass functions to quantify uncertainty in cluster membership. Various forms of prior information, including must-link and cannot-link constraints, supervised dissimilarities, and Distance Metric Learning, are incorporated to guide training and improve generalisation. By processing image pairs through shared network weights, DEEM aligns pairwise dissimilarities with the conflict between mass functions, thereby mitigating errors in noisy or incomplete distance matrices. Experiments on MNIST demonstrate that DEEM generalises effectively to unseen data while managing different types of prior knowledge, making it a promising approach for clustering and semi-supervised learning from image data under uncertainty.
DEEM (Deep evidence Encoding of iMages)是一种将信念函数与卷积神经网络结合在类似暹罗的框架中的聚类算法,用于无监督和半监督图像聚类。在DEEM中,图像被映射到Dempster-Shafer质量函数来量化聚类隶属度的不确定性。各种形式的先验信息,包括必须链接和不能链接约束,监督差异和距离度量学习,被纳入指导训练和提高泛化。通过共享网络权值来处理图像对,DEEM通过质量函数之间的冲突来对齐成对的不相似性,从而减轻了噪声或不完全距离矩阵的误差。在MNIST上的实验表明,在管理不同类型的先验知识的同时,DEEM对未见数据进行了有效的泛化,使其成为一种有前途的不确定图像数据聚类和半监督学习方法。
期刊介绍:
The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest.
Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning.
Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.