We propose here a strategy for the automatic annotation of outdoor photographs. Images are segmented in homogeneous regions which may be then assigned to seven different classes: sky, vegetation, snow, water, ground, street, and sand. These categories allows for content-aware image processing strategies. Our annotation strategy uses a normalized cut segmentation to identify the regions to be classified by a multi-class Support Vector Machine. The strategy has been evaluated on a set of images taken from the LabelMe dataset. © 2011 SPIE-IS&T.
Cusano, C., Schettini, R. (2011). Automatic annotation of outdoor photographs. In Proceedings SPIE Digital Photography VII. SPIE [10.1117/12.876653].
Automatic annotation of outdoor photographs
SCHETTINI, RAIMONDO
2011
Abstract
We propose here a strategy for the automatic annotation of outdoor photographs. Images are segmented in homogeneous regions which may be then assigned to seven different classes: sky, vegetation, snow, water, ground, street, and sand. These categories allows for content-aware image processing strategies. Our annotation strategy uses a normalized cut segmentation to identify the regions to be classified by a multi-class Support Vector Machine. The strategy has been evaluated on a set of images taken from the LabelMe dataset. © 2011 SPIE-IS&T.
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