Independent feature analysis for image retrieval

Content-based image retrieval methods based on the Euclidean metric expect the feature space to be isotropic. They suer from unequal dierential 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 dierential 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 while taking into consideration the correlation between features. This results in local neighborhoods that are constricted along feature dimensions and that are most relevant, while elongated along less relevant ones. In addition to exploring and exploiting local principal information, the system seeks a global space for ecient independent feature analysis by combining such local infor-mation. We provide experimental results that demonstrate the ecacy of our technique using both simulated and real