We propose a new Bayesian approach to object-based image retrieval with relevance feedback. Although estimating the object posterior probability density from few examples seems infeasible, we are able to approximate this density by exploiting statistics of the image database domain. Unlike previous approaches that assume an arbitrary distribution for the unconditional density of the feature vector (the density of the features taken over the entire image domain), we learn both the structure and the parameters of this density. These density estimates enable us to construct a Bayesian classifier. Using this Bayesian classifier, we perform a windowed scan over images for objects of interest and employ the user's feedback on the search results to train a second classifier that focuses on eliminating difficult false positives. We have incorporated this algorithm into an object-based image retrieval system. We demonstrate the effectiveness of our approach with experiments using a set of categories from the Corel database.
Object-based image retrieval using the statistical structure of images
2004-01-01
436546 byte
Conference paper
Electronic Resource
English
Object-Based Image Retrieval Using the Statistical Structure of Images
British Library Conference Proceedings | 2004
|Statistical Shape Features for Content-Based Image Retrieval
British Library Online Contents | 2002
|Fourier-Based Object Description in Defect Image Retrieval
British Library Online Contents | 2006
|Object-region-based color image retrieval [4551-34]
British Library Conference Proceedings | 2001
|Statistical Image Object Recognition using Mixture Densities
British Library Online Contents | 2001
|