We present a Bayesian framework for content-based image retrieval which models the distribution of color and texture features within sets of related images. Given a userspecified ...
Content-based image retrieval is a fairly new discipline. Yet research in this field has highlighted many approaches that show good performance in specific subproblems using singl...
Maria Grazia Albanesi, Marco Ferretti, Alessandro ...
The learning-enhanced relevance feedback has been one of the most active research areas in content-based image retrieval in recent years. However, few methods using the relevance ...
The thresholded t-map produced by the General Linear Model (GLM) gives an effective summary of activation patterns in functional brain images and is widely used for feature selecti...
Our work in content-based image retrieval (CBIR) relies on content-analysis of multiple representations of an image which we term multiple viewpoints or channels. The conceptual id...