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.
Relevance feedback decision trees in content-based image retrieval
2000-01-01
105674 byte
Conference paper
Electronic Resource
English
Relevance Feedback Decision Trees in Content-based Image Retrieval
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