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Memory‐color segmentation and classification using class‐specific eigenregions
Author(s) -
Fredembach Clément,
Estrada Francisco,
Süsstrunk Sabine
Publication year - 2009
Publication title -
journal of the society for information display
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 52
eISSN - 1938-3657
pISSN - 1071-0922
DOI - 10.1889/jsid17.11.921
Subject(s) - computer science , artificial intelligence , segmentation , pattern recognition (psychology) , task (project management) , consistency (knowledge bases) , computer vision , class (philosophy) , rendering (computer graphics) , management , economics
— Memory colors refer to the color of specific image regions that have the essential attribute of being perceived in a consistent manner by human observers. In color correction — or rendering — tasks, this consistency implies that they have to be faithfully reproduced; their importance, in that respect, is greater than that for other regions in an image. There are various schemes and attributes to detect memory colors, but the preferred method remains to segment the images into meaningful regions, a task for which many algorithms exist. Memory‐color regions are not, however, similar in their attributes. Significant variations in shape, size, and texture exist. As such, it is unclear whether a single segmentation algorithm is the most adapted for all of these classes. By using a large database of real‐world images, class‐specific geometrical features, eigenregions, were calculated. They can be used to evaluate how well an algorithm is adapted to segment a given class. A measure of localization of memory colors is given. The performance of class‐specific eigenregions were compared to general ones in the task of memory‐color‐region classification and it was observed that they provide a noticeable improvement in classification rates.