Feature Relevance Estimation for Image Databases

Content-based image retrieval methods based on the Euclidean metric expect the feature space to be isotropic. They suuer from unequal diierential relevance of features in computing the similarity between images in the input feature space. We propose a learning method that attempts to overcome this limitation by capturing local diierential relevance of features based on user feedback. This feedback, in the form of accept or reject examples generated in response to a query image, is used to locally estimate the strength of features along each dimension. This results in local neighborhoods that are constricted along feature dimensions that are most relevant, while enlongated along less relevant ones. We provide experimental results that demonstrate the eecacy of our technique using real-world data.