PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Content-Based Image Retrieval with Relevance Feedback Using Random Walks
Samuel Rota Bulò, Massimo Rabbi and Marcello Pelillo
Pattern Recognition Volume 44, Number 9, pp. 2109-2122, 2011.

Abstract

In this paper, we propose a novel approach to content-based image retrieval with relevance feedback, which is based on the random walker algorithm introduced in the context of interactive image segmentation. The idea is to treat the relevant and non-relevant images labeled by the user at every feedback round as “seed” nodes for the random walker problem. The ranking score for each unlabeled image is computed as the probability that a random walker starting from that image will reach a relevant seed before encountering a non-relevant one. Our method is easy to implement, parameter-free and scales well to large datasets. Extensive experiments on different real datasets with several image similarity measures show the superiority of our method over different recent approaches.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Theory & Algorithms
ID Code:9204
Deposited By:Marcello Pelillo
Deposited On:21 February 2012