Query reformulation for content based multimedia retrieval in MARS

Unlike traditional database management systems, in content-based multimedia retrieval databases, it is difficult for users to express their exact information need directly in a precise query. A typical interface allows users to express their information need using examples of objects similar to the ones they wish to retrieve. Such a user interface, however, requires mechanisms to learn the query representation from the examples. In this paper, we describe the query refinement framework implemented in the Multimedia Analysis and Retrieval System (MARS) for learning query representations using relevance feedback. The proposed framework uses a query expansion approach towards modifying the query representation in which relevant objects are added to the query. Furthermore, query reweighting techniques are used to adjust similarity functions.

[1]  Shi-Kuo Chang,et al.  Pictorial Data-Base Systems , 1981, Computer.

[2]  James Dowe,et al.  Content-based retrieval in multimedia imaging , 1993, Electronic Imaging.

[3]  John R. Smith,et al.  Searching for Images and Videos on the World-Wide Web , 1999 .

[4]  Thomas S. Huang,et al.  Supporting similarity queries in MARS , 1997, MULTIMEDIA '97.

[5]  Christos Faloutsos,et al.  MindReader: Querying Databases Through Multiple Examples , 1998, VLDB.

[6]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[7]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..

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

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

[10]  Ronald Fagin,et al.  Incorporating User Preferences in Multimedia Queries , 1997, ICDT.

[11]  Makoto Miyahara,et al.  Mathematical Transform Of (R, G, B) Color Data To Munsell (H, V, C) Color Data , 1988, Other Conferences.

[12]  B. S. Manjunath,et al.  Texture features and learning similarity , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  Ning-San Chang,et al.  A Relational Database System for Images , 1980, Pictorial Information Systems.

[14]  Shih-Fu Chang,et al.  Tools and techniques for color image retrieval , 1996, Electronic Imaging.

[15]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[16]  T.S. Huang,et al.  A relevance feedback architecture for content-based multimedia information retrieval systems , 1997, 1997 Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries.

[17]  Thomas S. Huang,et al.  Relevance feedback techniques in interactive content-based image retrieval , 1997, Electronic Imaging.

[18]  Thomas S. Huang,et al.  Automatic Matching Tool Selection Using Relevance Feedback In Mars , 1997 .

[19]  Ingemar J. Cox,et al.  An optimized interaction strategy for Bayesian relevance feedback , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[20]  Shih-Fu Chang,et al.  Automated binary texture feature sets for image retrieval , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[21]  Markus A. Stricker,et al.  Similarity of color images , 1995, Electronic Imaging.

[22]  John E. Howland,et al.  Computer graphics , 1990, IEEE Potentials.

[23]  David A. Hull.  Improving text retrieval for the routing problem using latent semantic indexing , 1994, SIGIR '94.

[24]  Thomas S. Huang,et al.  Modified Fourier Descriptors for Shape Representation - A Practical Approach , 1996 .

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

[26]  W. Bruce Croft,et al.  The INQUERY Retrieval System , 1992, DEXA.

[27]  Rosalind W. Picard,et al.  Interactive Learning Using a "Society of Models" , 2017, CVPR 1996.

[28]  S. Sclaroff,et al.  ImageRover: a content-based image browser for the World Wide Web , 1997, 1997 Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries.

[29]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[30]  Kannan Ramchandran,et al.  Multimedia Analysis and Retrieval System (MARS) Project , 1996, Data Processing Clinic.

[31]  Yong Rui,et al.  Multimedia Analysis and Retrieval System , 1997 .

[32]  Christos Faloutsos,et al.  QBIC project: querying images by content, using color, texture, and shape , 1993, Electronic Imaging.