Abstract
We present a novel approach to segment and classify objects in images into two classes. A binary conditional random field (CRF) framework is augmented with an unsupervised clustering step learning contextual relations of objects, the so-called implicit scene context (ISC). Several experiments with simulated data, images from benchmark data sets, and aerial images of an urban area show improved results compared to a standard CRF.
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Wegner, J.D., Rosenhahn, B., Sörgel, U. (2012). Segmentation and Classification of Objects with Implicit Scene Context. In: Dellaert, F., Frahm, JM., Pollefeys, M., Leal-Taixé, L., Rosenhahn, B. (eds) Outdoor and Large-Scale Real-World Scene Analysis. Lecture Notes in Computer Science, vol 7474. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34091-8_12
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DOI: https://doi.org/10.1007/978-3-642-34091-8_12
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