Abstract
CBIR has been an active topic for more than one decade. Current systems still lack in flexibility and accuracy because of semantic gap between image’s feature-level and semantic-level representations. Although many techniques have been developed for automatic or semi-automatic retrieval (e.g. interactive browsing, relevance feedback (RF)), issues about how to find suitable features and how to measure the image content still remain. It has been a challenging task to choose sound features for coding image content properly. This requires intensive interactive effort for discovering useful regularities between features and content semantics. In this paper, we present an interactive visualization system for supporting feature investigations. It allows users to choose different features, feature combinations, and representations for testing their impacts on measuring content-semantics. The experiments demonstrate how various perceptual edge features and groupings are interactively handled for retrieval measures. The system can be extended to include more features.
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Hu, G., Gao, Q. (2009). An Interactive Image Feature Visualization System for Supporting CBIR Study. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2009. Lecture Notes in Computer Science, vol 5627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02611-9_24
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DOI: https://doi.org/10.1007/978-3-642-02611-9_24
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