This Article 
 Bibliographic References 
 Add to: 
Eigenregions for Image Classification
December 2004 (vol. 26 no. 12)
pp. 1645-1649
For certain databases and classification tasks, analyzing images based on region features instead of image features results in more accurate classifications. We introduce eigenregions, which are geometrical features that encompass area, location, and shape properties of an image region, even if the region is spatially incoherent. Eigenregions are calculated using principal component analysis (PCA). On a database of 77,000 different regions obtained through the segmentation of 13,500 real-scene photographic images taken by nonprofessionals, eigenregions improved the detection of localized image classes by a noticeable amount. Additionally, eigenregions allow us to prove that the largest variance in natural image region geometry is due to its area and not to shape or position.

[1] C. Carson, S. Belongie, H. Greenspan, and J. Malik, “Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, pp. 1026-1038, 2002.
[2] S.-F. Chang, “Content-Based Indexing and Retrieval of Visual Information,” IEEE Signal Processing Magazine, vol. 14, pp. 45-48, 1997.
[3] Y.Q. Chen, “Novel Techniques for Image Texture Classification,” PhD Thesis, Univ. of Southampton, 1995.
[4] C. Faloutsos et al., “Efficient and Effective Querying by Image Content,” J. Intelligent Information Systems, vol. 3, pp. 231-262, 1994.
[5] C. Fredembach, “Salient Regions for Automatic Color Correction and Image Classification,” MSc Thesis, EPFL, 2003.
[6] C. Fredembach, M. Schröder, and S. Susstrunk, “Region-Based Image Classification for Automatic Color Correction,” Proc. IS&T 11th Color Imaging Conf., pp. 59-65, 2003.
[7] C.-S. Fuh, S.-W. Cho, and K. Essig, “Hierarchical Color Image Region Segmentation for Content-Based Image Retreival System,” IEEE Trans. Image Processing, vol. 9, pp. 156-162, 2000.
[8] K. Fukunaga, “Statistical Pattern Recognition,” Handbook of Pattern Recognition and Computer Vision, C. Chen, L. Pau and P. Wang, eds., pp. 33-60, World Scientific, 1993.
[9] R.M. Haralick, K. Shanmugan, and I. Dinstein, “Textural Features for Image Classification,” IEEE Trans. Systems, Man, and Cybernetics, vol. 3, pp. 610-621, 1973.
[10] A.K. Jain, Fundamentals of Digital Image Processing. Prentice-Hall Int'l, 1989.
[11] W. Ma and B. Manjunath, “EdgeFlow: A Technique for Boundary Detection and Image Segmentation,” IEEE Trans. Image Processing, vol. 9, pp. 1375-1388, 2000.
[12] MPEG Requirements Group, “Description of MPEG-7 Content Set,” ISO/IEC/JTC1/SC29/WG11/N2467, 2001.
[13] H. Palus, “Region-Based Colour Image Segmentation: Control Parameters and Evaluation Functions,” Proc. IS&T First European Conf. Color in Graphics, Imaging, and Vision, pp. 259-262, 2002.
[14] T. Pun and D. Squire, “Statistical Structuring of Pictorial Databases for Content-Based Image Retrieval Systems,” Pattern Recognition Letters, vol. 17, pp. 1299-1310, 1996.
[15] R. Ramamoorthi, “Analytic PCA Construction for Theoretical Analysis of Lighting Variability in Images of a Lambertian Object,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, pp. 1322-1333, 2002.
[16] M. Schroder and S. Moser, “Automatic Color Correction Based on Generic Content Based Image Analysis,” Proc. IS&T Ninth Color Imaging Conf., pp. 41-45, 2001.
[17] M. Schröder, “Stochastic Modeling of Image Content in Remote Sensing Image Archives,” PhD Thesis, ETHZ, 2000.
[18] A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-Based Image Retrieval at the End of the Early Years,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, pp. 1349-1380, 2000.
[19] M. Turk and A. Pentland, “Eigenfaces for Recognition,” J. Cognitive Neuroscience, vol. 3, pp. 71-96, 1991.
[20] P. Smits and A. Annoni, “Toward Specification-Driven Change Detection,” IEEE Trans. Geoscience and Remote Sensing, vol. 38, pp. 1484-1488, 2000.
[21] G. Wyszecki and W. Stiles, Color Science: Concept and Methods, Quantitative Data, and Formulae. Wiley, 1982.

Index Terms:
Eigenregions, image classification, region analysis, image features.
Cl?ment Fredembach, Michael Schr?der, Sabine S?sstrunk, "Eigenregions for Image Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 12, pp. 1645-1649, Dec. 2004, doi:10.1109/TPAMI.2004.123
Usage of this product signifies your acceptance of the Terms of Use.