Guest Editors' Introduction to the Special Section on Award Winning Papers from the IEEE CS Conference on Computer Vision and Pattern Recognition (CVPR)

The three articles in this special section are selected papers from the IEEE CS Conference on Computer Vision and Pattern Recognition that was held in Anchorage, AL, in June 2008.     

Supporting Content-Based Retrieval in Large Image Database Systems

In this paper, we investigate approaches to supporting effective and efficient retrieval of image data based on content. We firstintroduce an effective block-oriented image decomposition structure which can be used to represent image content inimage database systems. We then discuss theapplication of this image data model to content-based image retrieval.Using wavelet transforms to extract image features,significant content features can be extracted from image datathrough decorrelating the data in their pixel format into frequency domain. Feature vectors ofimages can then be constructed. Content-based image retrievalis performed by comparing the feature vectors of the query imageand the decomposed segments in database images.Our experimental analysis illustrates that the proposed block-oriented image representationoffers a novel decomposition structure to be used tofacilitate effective and efficient image retrieval.     

NetView: integrating large scale distributed visual databases

NetView supports global content-based query access to various visual databases over the Internet. An integrated metaserver consisting of a metadatabase, metasearch agent, and query manager facilitates such access. NetView significantly reduces the amount of time and effort that users spend in finding information of interest. It also can further refine the metaserver's performance based on user feedback