Model-Based Multi-Modal Information Retrieval from Large Archives

In this paper, we describe a new paradigm for information retrieval in which the retrieval target is based on a model. Three types of models – linear, finite state, and knowledge models are discussed. These information retrieval scenarios often arise from applications such as environmental epidemiology, oil/gas production and exploration, and precision agriculture/forestry. Traditional model-based data and information processing usually requires the processing of each and every data points. The proposed new framework, in contrast, will process the data progressively using a set of progressive models and utilize indexing techniques specialized for the model to facilitate retrieval, thus achieving a dramatic

[1]  Alexander Thomasian,et al.  Clustering and singular value decomposition for approximate indexing in high dimensional spaces , 1998, CIKM '98.

[2]  John R. Smith,et al.  Sequential processing for content-based retrieval of composite objects , 1997, Electronic Imaging.

[3]  Serge J. Belongie,et al.  Region-based image querying , 1997, 1997 Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries.

[4]  John R. Smith,et al.  The onion technique: indexing for linear optimization queries , 2000, SIGMOD '00.

[5]  Shih-Fu Chang,et al.  VisualSEEk: a fully automated content-based image query system , 1997, MULTIMEDIA '96.

[6]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  John R. Smith,et al.  Framework for efficient processing of content-based fuzzy Cartesian queries , 1999, Electronic Imaging.

[8]  Ming-Syan Chen,et al.  Progressive texture matching for Earth-observing satellite image database , 1996, Other Conferences.

[9]  John R. Smith,et al.  Adaptive storage and retrieval of large compressed images , 1998, Electronic Imaging.

[10]  Shih-Fu Chang,et al.  SaFe: a general framework for integrated spatial and feature image search , 1997, Proceedings of First Signal Processing Society Workshop on Multimedia Signal Processing.

[11]  Amarnath Gupta,et al.  Virage image search engine: an open framework for image management , 1996, Electronic Imaging.

[12]  John Turek,et al.  Progressive classification in the compressed domain for large EOS satellite databases , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[13]  David A. Forsyth,et al.  Body plans , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[15]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[16]  John R. Smith,et al.  An adaptive view element framework for multi-dimensional data management , 1999, CIKM '99.