A region-based image database system using colour and texture

Abstract In this paper, a region-based image database system is presented. When an image is entered into the database it is first clustered into similar looking regions using the local colour and texture properties of the image. The mean colour and texture properties of these regions along with their location and size are then stored as an index into that image. To query the database, the user specifies an object on which the same texture and colour properties are calculated. A neural network is trained on these features and then used to search through the indices of the database images. All similar looking regions in the images are shown as the results to the query.

[1]  Josef Kittler,et al.  Indexing an image database by shape content using curvature scale space , 1996 .

[2]  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.

[3]  Josef Kittler,et al.  Choosing an Optimal Neural Network Size to aid a Search Through a Large Image Database , 1998, BMVC.

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

[5]  Michael J. Swain,et al.  The capacity of color histogram indexing , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Dorin Comaniciu,et al.  Robust analysis of feature spaces: color image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Josef Kittler,et al.  A comparison of colour texture attributes selected by statistical feature selection and neural network methods , 1997, Pattern Recognit. Lett..

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

[9]  Machiko Sato,et al.  Fast accessing method of color image , 1993, Optics & Photonics.

[10]  Michael J. Swain,et al.  Interactive indexing into image databases , 1993, Electronic Imaging.

[11]  Yihong Gong,et al.  An image database system with content capturing and fast image indexing abilities , 1994, 1994 Proceedings of IEEE International Conference on Multimedia Computing and Systems.

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