Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised graph clustering: a kernel approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
International Journal of Approximate Reasoning
Aggregation Pheromone Density Based Pattern Classification
Fundamenta Informaticae
Image Analysis, Classification, and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL, Second Edition
Semi-Supervised Learning
Fuzzy clustering algorithms for unsupervised change detection in remote sensing images
Information Sciences: an International Journal
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
A Kernel Approach for Semisupervised Metric Learning
IEEE Transactions on Neural Networks
Semisupervised Learning Using Negative Labels
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
Scarcity of sufficient ground truth information is the primary bottleneck for adopting any supervised methodology in change detection domain and hence, unsupervised approaches are mostly used for this task. But, with a few labelled patterns in hand, semi-supervised methods can be chosen instead of unsupervised ones to utilise both the labelled and unlabelled patterns completely. Work on semi-supervised learning both in the areas of clustering and classification is now being explored. In this article, a detailed study has been made by applying some of the semi-supervised clustering techniques for change detection. In present investigation, five semi-supervised clustering techniques, namely COP-KMeans, seeded-KMeans, constrained-KMeans, semi-supervised-HMRF-KMeans and semi-supervised-kernel-KMeans algorithms are used. A comparative analysis has been made among these algorithms and standard K-Means algorithm, using two multi-temporal remotely sensed images and are also statistically validated using paired t-test. Experimental results conclude that constrained-KMeans for both the datasets is more applicable for change detection than COP-KMeans and seeded-KMeans. Semi-supervised-HMRF-KMeans and semi-supervised-kernel-KMeans algorithms are found not to be robust for all the datasets because these algorithms outperform constrained-KMeans in case of only one dataset.