Image Clustering without Ground Truth

2016-10-25 · Abhisek Dash, Sujoy Chatterjee, Tripti Prasad, Malay Bhattacharyya

Cluster analysis has become one of the most exercised research areas over the past few decades in computer science. As a consequence, numerous clustering algorithms have already been developed to find appropriate partitions of a set of objects. Given multiple such clustering solutions, it is a challenging task to obtain an ensemble of these solutions. This becomes more challenging when the ground truth about the number of clusters is unavailable. In this paper, we introduce a crowd-powered model to collect solutions of image clustering from the general crowd and pose it as a clustering ensemble problem with variable number of clusters. The varying number of clusters basically reflects the crowd workers' perspective toward a particular set of objects. We allow a set of crowd workers to independently cluster the images as per their perceptions. We address the problem by finding out centroid of the clusters using an appropriate distance measure and prioritize the likelihood of similarity of the individual cluster sets. The effectiveness of the proposed method is demonstrated by applying it on multiple artificial datasets obtained from crowd.

📄 PDF Abstract BibTeX arXiv:1610.07758

Code (0)

등록된 구현이 없습니다.

Tasks

ClusteringClustering Ensemble Image Clustering

Deep Superpixel Generation and Clustering for Weakly Supervised Segmentation of Brain Tumors in MR Images

2022-09-20 · Jay J. Yoo, Khashayar Namdar, Farzad Khalvati

Training machine learning models to segment tumors and other anomalies in medical images is an important step for developing diagnostic tools but generally requires manually annotated ground truth segmentations, which ne…

Binary Classification Brain Tumor Segmentation Clustering Diagnostic+4

SCAN: Learning to Classify Images without Labels

2020-05-25 · ECCV 2020 8 · Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans 외

Can we automatically group images into semantically meaningful clusters when ground-truth annotations are absent? The task of unsupervised image classification remains an important, and open challenge in computer vision.…

Classification Clustering General Classification image-classification+5

Contrastive Hierarchical Clustering

2023-03-03 · Michał Znaleźniak, Przemysław Rola, Patryk Kaszuba, Jacek Tabor 외

Deep clustering has been dominated by flat models, which split a dataset into a predefined number of groups. Although recent methods achieve an extremely high similarity with the ground truth on popular benchmarks, the i…

Clustering Deep Clustering Image Clustering Self-Supervised Learning

Can an unsupervised clustering algorithm reproduce a categorization system?

2024-08-19 · Nathalia Castellanos, Dhruv Desai, Sebastian Frank, Stefano Pasquali 외

Peer analysis is a critical component of investment management, often relying on expert-provided categorization systems. These systems' consistency is questioned when they do not align with cohorts from unsupervised clus…

Clustering feature selection Management Metric Learning

An Adaptor for Triggering Semi-Supervised Learning to Out-of-Box Serve Deep Image Clustering

2025-09-25 · Yue Duan, Lei Qi, Yinghuan Shi, Yang Gao arxiv

Recently, some works integrate SSL techniques into deep clustering frameworks to enhance image clustering performance. However, they all need pretraining, clustering learning, or a trained clustering model as prerequisit…

Image Clustering Deep Clustering