State-of-the-Art in Content-Based Image and Video Retrieval / Edition 1 available in Paperback
Images and video play a crucial role in visual information systems and multimedia. There is an extraordinary number of applications of such systems in entertainment, business, art, engineering, and science. Such applications often involved large image and video collections, and therefore, searching for images and video in large collections is becoming an important operation. Because of the size of such databases, efficiency is crucial. We strongly believe that image and video retrieval need an integrated approach from fields such as image processing, shape processing, perception, database indexing, visualization, and querying, etc. This book contains a selection of results that was presented at the Dagstuhl Seminar on Content-Based Image and Video Retrieval, in December 1999. The purpose of this seminar was to bring together people from the various fields, in order to promote information exchange and interaction among researchers who are interested in various aspects of accessing the content of image and video data. The book provides an overview of the state of the art in content-based image and video retrieval. The topics covered by the chapters are integrated system aspects, as well as techniques from image processing, computer vision, multimedia, databases, graphics, signal processing, and information theory. The book will be of interest to researchers and professionals in the fields of multimedia, visual information (database) systems, computer vision, and information retrieval.
Table of ContentsPreface. 1. Image Content Analysis and Description; X. Zabulis, S.C. Orphanoudakis. 2. Local Features for Image Retrieval; L. Van Gool, et al. 3. Fast Invariant Feature Extraction for Image Retrieval; S. Siggelkow, H. Burkhardt. 4. Shape Description and Search for Similar Objects in Image Databases; L.J. Latecki, R. Lakaemper. 5. Features in Content-based Image Retrieval Systems: a Survey; R.C. Veltkamp, et al. 6. Probablistic Image Models for Object Recognition and Pose Estimation; J. Hornegger, H. Niemann. 7. Distribution-based Image Similarity; J. Puzicha. 8. Distribution Free Statistics for Segmentation; G. Frederix, E.J. Pauwels. 9. Information Retrieval Methods for Multimedia Objects; N. Fuhr. 10. New descriptors for image and video indexing; P. Gros, et al. 11. Facial and Motion Analysis for Image and Video Retrieval; M. Tistarelli, E. Grosso. 12. Asymmetric Similarity Measures for Video Summarisation; S.M. Iacob, et al. 13. Video Retrieval using Semantic Data; A. Del Bimbo. 14. Adaptable Similarity Search in Large Image Databases; T. Seidl, H.-P. Kriegel. 15. Parallel NN-search for large multimedia repositories; R. Weber, et al.