PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

HEAT: Iterative Relevance Feedback with One Million Images
Nicolae Suditu and Francois Fleuret
In: IEEE International Conference on Computer Vision(2011).


It has been shown repeatedly that iterative relevance feedback is a very efficient solution for content-based image retrieval. However, no existing system scales gracefully to hundreds of thousands or millions of images. We present a new approach dubbed Hierarchical and Expandable Adaptive Trace (HEAT) to tackle this problem. Our approach modulates on-the-fly the resolution of the interactive search in different parts of the image collection, by relying on a hierarchical organization of the images computed off-line. Internally, the strategy is to maintain an accurate approximation of the probabilities of relevance of the individual images while fixing an upper bound on the required computation. Our system is compared on the ImageNet database to the state-of-the-art approach it extends, by conducting user evaluations on a sub-collection of 33,000 images. Its scalability is then demonstrated by conducting similar evaluations on 1,000,000 images.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Information Retrieval & Textual Information Access
ID Code:9367
Deposited By:Francois Fleuret
Deposited On:16 March 2012