Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Efficient resource selection in distributed visual information systems
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Internet Resource Discovery Services
Computer
Fast retrieval of high-dimensional feature vectors in P2P networks using compact peer data summaries
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Journal of Cognitive Neuroscience
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
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With the wide diffusion of digital image acquisition devices, the cost of managing hundreds of digital images is quickly increasing. Currently, the main way to search digital image libraries is by keywords given by the user. However, users usually add ambiguos keywords for large set of images. A content-based system intended to automatically find a query image, or similar images, within the whole collection is needed. In our work we address the scenario where medical image collections, which nowadays are rapidly expanding in quantity and heterogeneity, are shared in a distributed system to support diagnostic and preventive medicine. Our goal is to produce an efficient content-based description of each image collection in order to perform content-based image retrieval (CBIR) just in the node where the searched images are supposed to be. A novel combined fuzzy and probabilistic data descriptor is presented and experimental results are illustrated.