Abstract
This paper presents a novel approach for representation of image contents based on edge structural features. Edge detection is carried out for an image in the pre-processing stage. For feature representation, edge pixels are grouped into a set of segments through geometrical partitioning of the whole edge image. Then the invariant feature vector is computed for each edge-pixel segment. Thereby the image is represented with a set of spatially distributed feature vectors, each of which describes the local pattern of edge structures. Matching of two images can be achieved by the correspondence of two sets of feature vectors. Without the difficulty of image segmentation and object extraction due to the complexity of the real world images, the proposed approach provides a simple and flexible description for the image with complex scene, in terms of structural features of the image content. Experiments with real images illustrate the effectiveness of this new method.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
W. Y. Ma, B. S. Manjunath, NETRA: A toolbox for navigating large image databases, Proc. of IEEE International Conference on Image Processing, Santa Barbara, California, USA, Jan., 1997, 925–928.
G. Lu, S. Teng, A novel image retrieval technique based on vector quantization, Proc. of International Conference on Computational Intelligence for Modelling, Control and Automation, Vienna, Austria, 1999, 36–41.
Lei Zhu, Aidong Zhang, Aibing Rao, Rohini Srihari, Keyblock: An approach for content-based image retrieval, Proc. of ACM Multimedia 2000, Los Angeles, California, USA, 2000, 157–166.
B. Moghaddam, et al., Region-of-interest and spatial layout for content-based image retrieval, Multimedia Tools & Applications, Kluwer Academic Publishers, 14(2001)3, 201–210.
J. T. Laaksonen, J. M. Koskela, S. P. Laakso, E. Oja, PicSOM-content-based image retrieval with self-organizing maps, Pattern Recognition Letters, 21(2000)13/14, 1199–1207.
D. Squire, et al., Content-based query of image databases, inspirations from text retrieval: Inverted files, frequency-based weights and relevance feedback, Pattern Recognition Letters (Selected Papers from the 11th Scandinavian Conference on Image Analysis SCIA ’99), 21(2000)13/14, 1193–1198.
X. S. Zhou, T. S. Huang, Edge/structural features for content based image retrieval, Pattern Recognition Letters, 22(2001)5, 457–468.
A. W. M. Smeulders, et al., Content-based image retrieval at the end of the early years, IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(2000)12, 1349–1380.
S. Sclaroff, A. P. Pentland, Modal matching for correspondence and recognition, IEEE Trans. on Pattern Analysis and Machine Intelligence, 17(1995)6, 545–561.
L. Shapiro, O. Brady, Feature-based correspondence: An eigenvector approach, Image and Vision Computing, 10(1992)5, 283–288.
Author information
Authors and Affiliations
Additional information
Supported by SRF for ROCS, SEM and the Funds for Young Scientists of Shandong Province.
About this article
Cite this article
Jia, X., Wang, G. New approach for image representation based on geometric structural contents. J. of Electron.(China) 20, 431–438 (2003). https://doi.org/10.1007/s11767-003-0057-z
Received:
Revised:
Issue Date:
DOI: https://doi.org/10.1007/s11767-003-0057-z