Constrained querying of multimedia databases: issues and approaches

This paper investigates the problem of high-level querying of multimedia data by imposing arbitrary domain-specific constraints among multimedia objects. We argue that the current structured query mode, and the query-by-content model, are insufficient for many important applications, and we propose an alternative query framework that unifies and extends the previous two models. The proposed framework is based on the querying-by-concept paradigm, where the query is expressed simply in terms of concepts, regardless of the complexity of the underlying multimedia search engines. The query-by-concept paradigm was previously illustrated by the CAMEL system. The present paper builds upon and extends that work by adding arbitrary constraints and multiple levels of hierarchy in the concept representation model. We consider queries simply as descriptions of virtual data set, and that allows us to use the same unifying concept representation for query specification, as well as for data annotation purposes. We also identify some key issues and challenges presented by the new framework, and we outline possible approaches for overcoming them. In particular, we study the problems of concept representation, extraction, refinement, storage, and matching.

[1]  Jeffrey Scott Vitter,et al.  CAMEL: concept annotated image libraries , 2001, IS&T/SPIE Electronic Imaging.

[2]  Ronald Fagin,et al.  Combining Fuzzy Information from Multiple Systems , 1999, J. Comput. Syst. Sci..

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

[4]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[5]  Amarnath Gupta,et al.  Visual information retrieval , 1997, CACM.

[6]  Rosalind W. Picard,et al.  Finding similar patterns in large image databases , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[7]  H. Chertkow,et al.  Semantic memory , 2002, Current neurology and neuroscience reports.

[8]  David E. Rumelhart,et al.  Predicting the Future: a Connectionist Approach , 1990, Int. J. Neural Syst..

[9]  Thomas S. Huang,et al.  Supporting Ranked Boolean Similarity Queries in MARS , 1998, IEEE Trans. Knowl. Data Eng..

[10]  Shih-Fu Chang,et al.  Querying by color regions using VisualSEEk content-based visual query system , 1997 .

[11]  John R. Smith,et al.  New frontiers for intelligent content-based retrieval , 2001, IS&T/SPIE Electronic Imaging.

[12]  G. Kane Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .

[13]  Sharad Mehrotra,et al.  Information Retrieval over Multimedia Documents , 1999 .

[14]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[15]  Ronald Fagin,et al.  Fuzzy queries in multimedia database systems , 1998, PODS '98.

[16]  Euripides G. M. Petrakis,et al.  Similarity Searching in Medical Image Databases , 1997, IEEE Trans. Knowl. Data Eng..

[17]  Shih-Fu Chang,et al.  MediaNet: a multimedia information network for knowledge representation , 2000, SPIE Optics East.

[18]  David Salesin,et al.  Fast multiresolution image querying , 1995, SIGGRAPH.

[19]  Yves Chauvin,et al.  Backpropagation: theory, architectures, and applications , 1995 .

[20]  Joseph Montanarella,et al.  Artificial Intelligence : A Knowledge-Based Approach , 1996 .

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

[22]  K. Wakimoto,et al.  Efficient and Effective Querying by Image Content , 1994 .

[23]  John R. Smith,et al.  Intelligent multimedia information retrieval , 1997 .

[24]  John P. Oakley,et al.  Storage and Retrieval for Image and Video Databases , 1993 .

[25]  David E. Rumelhart,et al.  Generalization by Weight-Elimination with Application to Forecasting , 1990, NIPS.

[26]  Jitendra Malik,et al.  Blobworld: A System for Region-Based Image Indexing and Retrieval , 1999, VISUAL.

[27]  James Ze Wang,et al.  Content-based image indexing and searching using Daubechies' wavelets , 1998, International Journal on Digital Libraries.

[28]  Hans Henrik Thodberg,et al.  Improving Generalization of Neural Networks Through Pruning , 1991, Int. J. Neural Syst..

[29]  Shi-Kuo Chang,et al.  Iconic Indexing by 2-D Strings , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Shih-Fu Chang,et al.  Integrated spatial and feature image query , 1999, Multimedia Systems.