Abstract
In this paper we present an approach for image region classification that combines low-level processing with high-level scene understanding. For the low-level training, predefined image concepts are statistically modelled using wavelet features extracted directly from image pixels. For classification, features of a given test region compared with these statistical models provide probabilistic evaluations for all possible image concepts. Maximising these values themselves already leads to a classification result (label). However, in our paper they are used as an input for the high-level approach exploiting explicitly represented spatial arrangements of labels, so called spatial prototypes. We formalise the problem using Fuzzy Constraint Satisfaction Problems and Linear Programming. They provide a model with explicit knowledge that is suitable to aid the task of region labelling. Experiments performed for nearly 6000 test image regions show that combining low-level and high-level image analysis increases the labelling accuracy significantly.
The research activity leading to this work has been supported by the European Commission under the contract FP6-027026-K-SPACE.
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Saathoff, C., Grzegorzek, M., Staab, S. (2008). Labelling Image Regions Using Wavelet Features and Spatial Prototypes. In: Duke, D., Hardman, L., Hauptmann, A., Paulus, D., Staab, S. (eds) Semantic Multimedia. SAMT 2008. Lecture Notes in Computer Science, vol 5392. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92235-3_9
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DOI: https://doi.org/10.1007/978-3-540-92235-3_9
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