The paper describes an innovative image annotation tool, based on a multi-class Support Vector Machine, for classifying image pixels in one of seven classes - sky, skin, vegetation, snow, water, ground, and man-made structures - or as unknown. These visual categories mirror high-level human perception, permitting the design of intuitive and effective color and contrast enhancement strategies. As a pre-processing step, a smart color balancing algorithm is applied, making the overall procedure suitable for uncalibrated images, such as images acquired by unknown systems under unknown lighting conditions.
Schettini, R., Gasparini, F., Cusano, C. (2005). Image annotation for adaptive enhancement of uncalibrated color images. In 8th International Conference on Visual Information Systems (pp.216-225). Springer [10.1007/11590064_19].
Image annotation for adaptive enhancement of uncalibrated color images
SCHETTINI, RAIMONDO;GASPARINI, FRANCESCA;CUSANO, CLAUDIO
2005
Abstract
The paper describes an innovative image annotation tool, based on a multi-class Support Vector Machine, for classifying image pixels in one of seven classes - sky, skin, vegetation, snow, water, ground, and man-made structures - or as unknown. These visual categories mirror high-level human perception, permitting the design of intuitive and effective color and contrast enhancement strategies. As a pre-processing step, a smart color balancing algorithm is applied, making the overall procedure suitable for uncalibrated images, such as images acquired by unknown systems under unknown lighting conditions.
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