Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A tutorial on spectral clustering
Statistics and Computing
Adaptive Algorithms for Interactive Multimedia
IEEE MultiMedia
Ontology based personalized route planning system using a multi-criteria decision making approach
Expert Systems with Applications: An International Journal
Journal of Artificial Intelligence Research
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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Personalized navigation of 3D large scale geo-referred scenes has a tremendous impact in digital cultural heritage. This is a result of the recent progress in digitization technology which leads to the creation of massive digital geographic libraries. However, an efficient personalized 3D geo-referred architecture requires intelligent and on-line learning strategies able to dynamically capture user's preferences dynamics. In this paper, we propose an adaptive spectral learning framework towards 3D navigation of geo-referred scenes. Spectral clustering presents advantages compared to traditional center-based partitioning methods, such as the k-means; it effectively categorize non-Gaussian, complex distributions, present invariability to shapes and densities and it does not depend on the similarity metric used since learning is performed through similarity matrices by exploiting pair-wise comparisons. The main difficulty, however, in incorporating spectral learning in a 3D navigation architecture is its static implementation. To handle this difficulty, we propose in this paper an adaptive framework through the use of adaptive spectral learning which tailors 3D navigation to user's current needs.