Predictive clustering for image annotation and retrieval
(Jozef Stefan Institute, Slovenia)
Predictive clustering for image annotation and retrieval The predictive
clustering paradigm unifies the predominant machine learning approaches of
predictive modeling and clustering. It can solve a variety of problems of
predicting structured outputs, such as multi-target regression ahd hierarchical
multi-label classification. It elegantly handles different amounts of
supervision in learning, ranging from fully supervised, via semi-supervised, to
completely unsupervised learning.
We have used predictive clustering in bag-of-visual-words approaches to image
annotation and retrieval. It was used to learn to annotate images in
(hierarchical) multi-label settings. It was also used to construct visual
codebooks, which were then used for image annotation and retrieval. This
produced visual codebooks with superior discriminative power and thus better
retrieval performance, while maintaining excellent computational efficiency.