7 May 2003 Hybrid approach to classifying sky regions in natural images
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Sky is among the semantic object classes frequently seen in photographs and useful for image understanding, processing, and retrieval. We propose a novel hybrid approach to sky detection; based on color and texture classification, region extraction, and physics motivated sky signature validation. Sky can be of many different types; clear blue sky, cloudy/overcast sky, mixed sky, and twilight sky, etc. A single model cannot correctly characterize all the various types of skies due to the large difference in physics and appearance associated with different sky types. We have developed a set of physics-motivated sky models to identify clear blue-sky regions and cloudy/overcast sky regions. An exemplar-based approach is to generate the initial set of candidate sky regions. Another data-derived model is subsequently used to combine the results for different sky types to form a more complete sky map. Extensive testing using more than 3000 (randomly oriented) natural images shows that our comprehensive sky detector is able to accurately recall approximately 96% of all sky regions in the image set, with a precision of about 92%. Assuming correct image orientation, the precision on the same set of images increases to about 96%.
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Amit Singhal and Jiebo Luo "Hybrid approach to classifying sky regions in natural images", Proc. SPIE 5022, Image and Video Communications and Processing 2003, (7 May 2003); doi: 10.1117/12.476455; https://doi.org/10.1117/12.476455

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