Detecting Regular Structures for Invariant Retrieval

Many of the existing approaches to invariant content-based image retrieval rely on local features, such as color or specific intensity patterns (interest points). In some methods, structural content is introduced by using particular spatial configurations of these features, which are typical for the pattern considered. Such approaches are limited in their capability to deal with regular structures when high degree of invariance is required. Recently, we have proposed a general measure of pattern regularity [2] that is stable under weak perspective of non-flat patterns and varying illumination. In this paper we apply this measure to invariant detection of regular structures in aerial imagery.

[1]  Ioannis Pitas,et al.  Digital Image Processing Algorithms , 1993 .

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

[3]  Dmitry Chetverikov,et al.  Texture analysis using feature-based pairwise interaction maps , 1999, Pattern Recognit..

[4]  Dmitry Chetverikov Pattern regularity as a visual key , 2000, Image Vis. Comput..

[5]  Dmitry Chetverikov Structural filtering with texture feature-based interaction maps: fast algorithm and applications , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[6]  Cordelia Schmid,et al.  Local Grayvalue Invariants for Image Retrieval , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  R. Manmatha,et al.  Image retrieval by appearance , 1997, SIGIR '97.

[8]  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.

[9]  Dmitry Chetverikov,et al.  Texture feature based interaction maps and structural filtering , 1996 .

[10]  Andrew Zisserman,et al.  Appendix—projective geometry for machine vision , 1992 .

[11]  Arnold W. M. Smeulders,et al.  Color Based Object Recognition , 1997, ICIAP.