Applied multivariate statistical analysis
Applied multivariate statistical analysis
ASSERT: a physician-in-the-loop content-based retrieval system for HRCT image databases
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Computer and Robot Vision
Digital Picture Processing
A Knowledge-Based Approach for Retrieving Images by Content
IEEE Transactions on Knowledge and Data Engineering
Fast and Effective Retrieval of Medical Tumor Shapes
IEEE Transactions on Knowledge and Data Engineering
Local versus Global Features for Content-Based Image Retrieval
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Interactive content-based image retrieval using relevance feedback
Computer Vision and Image Understanding
SmartAlbum: a multi-modal photo annotation system
Proceedings of the tenth ACM international conference on Multimedia
Unsupervised Feature Selection Applied to Content-Based Retrieval of Lung Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Benchmarking in CBIR
Multimedia Tools and Applications
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We have addressed the following question in this contribution: To what extent should the domain experts, in our case physicians, be believed with regard to what they claim to see in images that allows them to recognize different types of pathology?Until recently our approach was to have a physician delineate the pathology bearing regions in the images. We then used what could be referred to as a scattershot approach to the characterization of these regions, meaning that we'd extract a very large number of features from these regions. Subsequently, we'd reduce the dimensionality of this feature space by using standard search techniques, such as the Sequential Forward Selection method.This contribution represents an alternative to the scattershot approach to initial feature extraction. In this paper, we first describe the perceptual categories that the physicians claim to use for classifying images as belonging to different diseases. We then describe the specific low-level features that need to be extracted to determine the presence or the absence of the various perceptual categories. We subsequently show the discriminatory power of the perceptual categories by presenting retrieval results obtained when a query image is matched with the database images on the basis of the presence or the absence of the various perceptual categories.