CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2004 vol.26 Issue No.12 - December
Issue No.12 - December (2004 vol.26)
Cl?ment Fredembach , IEEE
Michael Schr?der , IEEE
Sabine S?sstrunk , IEEE
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.
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 & Machine Intelligence, vol.26, no. 12, pp. 1645-1649, December 2004, doi:10.1109/TPAMI.2004.123