RISE: A Robust Image Search Engine

Image database indexing is used to improve the efficiency of image retrieval in response to a query expressed as an example image. The query image is processed to extract information that is matched against similar information from images stored in a database to retrieve a set of matched images. The matching process is achieved by a search engine. This paper advances robust image search engine (RISE). RISE is based on a technique that facilitates content similarity based retrieval of images using an index built on color components of selected blocks of images. It uses a measure of distance between the query and the images in the database. RISE builds on the JPEG indexing algorithm, extending it to formats other than JPEG and into color spaces, in addition to using a relational database. We have implemented RISE as a browser-based search engine, deployed as a Java servlet

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

[2]  Charles Poynton.  A Guided Tour of Color Space , 1997 .

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

[4]  James D. Murray,et al.  Encyclopedia of graphics file formats (2nd ed.) , 1996 .

[5]  Kyuseok Shim,et al.  WALRUS: A Similarity Retrieval Algorithm for Image Databases , 2004, IEEE Trans. Knowl. Data Eng..

[6]  Hanan Samet,et al.  The Quadtree and Related Hierarchical Data Structures , 1984, CSUR.

[7]  Louis G. Vuurpijl,et al.  Design guidelines for a Content-Based Image Retrieval color-selection interface , 2004 .

[8]  Sharlee Climer,et al.  Image database indexing using JPEG coefficients , 2002, Pattern Recognit..

[9]  Shih-Fu Chang,et al.  Visual information retrieval from large distributed online repositories , 1997, CACM.

[10]  Hayit Greenspan,et al.  Color- and Texture-based Image Segmentation Using the Expectation-Maximization Algorithm and its Application to Content-Based Image Retrieval. , 1998, ICCV 1998.

[11]  James D. Murray,et al.  Encyclopedia of graphics file formats , 1994 .

[12]  Athanassios N. Skodras.  Direct transform to transform computation , 1999, IEEE Signal Processing Letters.

[13]  Jitendra Malik,et al.  Color- and texture-based image segmentation using EM and its application to content-based image retrieval , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[14]  Robert Marti,et al.  Which is the best way to organize/classify images by content? , 2007, Image Vis. Comput..

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

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

[17]  D. Forsyth,et al.  Searching for Digital Pictures , 1997 .

[18]  Joan L. Mitchell,et al.  JPEG: Still Image Data Compression Standard , 1992 .

[19]  Gio Wiederhold,et al.  Semantics-sensitive integrated matching for picture libraries and biomedical image databases , 2000 .