Abstract
In this paper we present a framework for simultaneous image segmentation and region labeling leading to automatic image annotation. The proposed framework operates at semantic level using possible semantic labels to make decisions on handling image regions instead of visual features used traditionally. In order to stress its independence of a specific image segmentation approach we applied our idea on two region growing algorithms, i.e. watershed and recursive shortest spanning tree. Additionally we exploit the notion of visual context by employing fuzzy algebra and ontological taxonomic knowledge representation, incorporating in this way global information and improving region interpretation. In this process, semantic region growing labeling results are being re-adjusted appropriately, utilizing contextual knowledge in the form of domain-specific semantic concepts and relations. The performance of the overall methodology is demonstrated on a real-life still image dataset from the popular domains of beach holidays and motorsports.
This research was partially supported by the European Commission under contract FP6-001765 aceMedia and contract FP6-027026 K-SPACE and by the Greek Secretariat of Research and Technology (PENED Ontomedia 03 EΔ 475).
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Athanasiadis, T., Mylonas, P., Avrithis, Y. (2006). A Context-Based Region Labeling Approach for Semantic Image Segmentation. In: Avrithis, Y., Kompatsiaris, Y., Staab, S., O’Connor, N.E. (eds) Semantic Multimedia. SAMT 2006. Lecture Notes in Computer Science, vol 4306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11930334_17
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DOI: https://doi.org/10.1007/11930334_17
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