Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Using confidence bounds for exploitation-exploration trade-offs
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Image ranking with implicit feedback from eye movements
Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
Learning convex combinations of continuously parameterized basic kernels
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Hi-index | 0.00 |
In content-based image retrieval (CBIR) with relevance feedback we would like to retrieve relevant images based on their content features and the feedback given by users. In this paper we view CBIR as an Exploration-Exploitation problem and apply a kernel version of the LinRel algorithm to solve it. By using multiple feature extraction methods and utilising the feedback given by users, we adopt a strategy of multiple kernel learning to find a relevant feature space for the kernel LinRel algorithm. We call this algorithm LinRelMKL. Furthermore, when we have access to eye movement data of users viewing images we can enrich our (multiple) feature spaces by using a tensor kernel SVM. When learning in this enriched space we show that we can significantly improve the search results over the LinRel and LinRelMKL algorithms. Our results suggest that the use of exploration-exploitation with multiple feature spaces is an efficient way of constructing CBIR systems, and that when eye movement features are available, they should be used to help improve CBIR.