A scalable integrated region-based image retrieval system

We present a scalable algorithm for indexing and retrieving images based on region segmentation. The method uses statistical clustering on region features and IRM (integrated region matching), a measure developed to evaluate overall similarity between images. It incorporates properties of all the regions in the images by a region-matching scheme. The algorithm has been implemented as a part of our experimental SIMPLIcity (Semantics-sensitive Integrated Matching for Picture LIbraries) image retrieval system and tested on large-scale image databases of both general-purpose images and pathology slides. Experiments have demonstrated that this technique maintains the accuracy of the original system while reducing the matching time significantly.

[1]  John R. Smith,et al.  Image Classification and Querying Using Composite Region Templates , 1999, Comput. Vis. Image Underst..

[2]  Thomas S. Huang,et al.  Supporting content-based queries over images in MARS , 1997, Proceedings of IEEE International Conference on Multimedia Computing and Systems.

[3]  James Ze Wang,et al.  Semantics-sensitive Retrieval for Digital Picture Libraries , 1999, D Lib Mag..

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

[5]  Howard D. Wactlar,et al.  Informedia: improving access to digital video , 1994, INTR.

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

[7]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[8]  Sougata Mukherjea,et al.  AMORE: a world-wide web image retrieval engine , 1999, CHI Extended Abstracts.

[9]  Shih-Fu Chang,et al.  Image and video search engine for the World Wide Web , 1997, Electronic Imaging.

[10]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  James Ze Wang,et al.  IRM: integrated region matching for image retrieval , 2000, ACM Multimedia.

[12]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

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

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

[15]  B. S. Manjunath,et al.  NeTra: A toolbox for navigating large image databases , 1997, Proceedings of International Conference on Image Processing.

[16]  Maurice K. Wong,et al.  Algorithm AS136: A k-means clustering algorithm. , 1979 .

[17]  C. Tomasi.  The Earth Mover's Distance, Multi-Dimensional Scaling, and Color-Based Image Retrieval , 1997 .

[18]  James Ze Wang,et al.  Unsupervised Multiresolution Segmentation for Images with Low Depth of Field , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

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