Applied Connectionistic Methods in Computer Vision to Compare Segmented Images

Two similarity measures to compare whole and parts of images are proposed. These measures consider the color, shape and texture properties of image segments as well as their relative positions mutually.

[1]  Ze-Nian Li,et al.  Illumination Invariance and Object Model in Content-Based Image and Video Retrieval , 1999, J. Vis. Commun. Image Represent..

[2]  Heiko Wersing.  Spatial feature binding and learning in competitive neural layer architectures , 2000 .

[3]  Bohdan Zelinka,et al.  On a certain distance between isomorphism classes of graphs , 1975 .

[4]  Kristina Schädler,et al.  Comparing Structures Using a Hopfield-Style Neural Network , 1999, Applied Intelligence.

[5]  A. Winter,et al.  Differential feature distribution maps for image segmentation and region queries in image databases , 1999, Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries (CBAIVL'99).

[6]  Azriel Rosenfeld,et al.  Scene Labeling by Relaxation Operations , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  Thomas Sikora,et al.  The MPEG-7 visual standard for content description-an overview , 2001, IEEE Trans. Circuits Syst. Video Technol..

[8]  Joachim Weickert.  Fast Segmentation Methods Based on Partial Differential Equations and the Watershed Transformation , 1998, DAGM-Symposium.

[9]  M. I. Schlesinger,et al.  Some solvable subclasses of structural recognition problems , 2000 .

[10]  Chiou-Shann Fuh,et al.  Hierarchical color image region segmentation for content-based image retrieval system , 2000, IEEE Trans. Image Process..

[11]  B. S. Manjunath,et al.  Edge flow: A framework of boundary detection and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.