Relevance feedback decision trees in content-based image retrieval

Abstract

Significant time and effort has been devoted to finding feature representations of images in databases in order to enable content-based image retrieval (CBIR). Relevance feedback is a mechanism for improving retrieval precision over time by allowing the user to implicitly communicate to the system which of these features are relevant and which are not. We propose a relevance feedback retrieval system that, for each retrieval iteration, learns a decision tree to uncover a common thread between all images marked as relevant. This tree is then used as a model for inferring which of the unseen images the user would not likely desire. We evaluate our approach within the domain of HRCT images of the lung.