We propose a region-based method for the annotation of outdoor photographs. First, images are oversegmented using the normalized cut algorithm. Each resulting region is described by color and texture features, and is then classified by a multi-class Support Vector Machine into seven classes: sky, vegetation, snow, water, ground, street, and sand. Finally, a rejection option is applied to discard those regions for which the classifier is not confident enough. For training and evaluation we used more than 12,000 images taken from the LabelMe project. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Cusano, C. (2011). Region-based annotation of digital photographs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6626 LNCS, pp. 47–59). https://doi.org/10.1007/978-3-642-20404-3_4
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