Abstract
This paper presents an interactive hierarchical visualization system for an image retrieval application. This visualization system needs to present the similarities of images. Furthermore, it is required to provide an easy way to explore and navigate images’ feature space at different levels of detail. Our system utilizes a Multi-layer Geodesic Self-Organizing Map (GeodesicSOM) and Learning Vector Quantization (LVQ) to increase the accuracy in data representation at different levels of detail. The Multi-layer GeodesicSOM provides fast access/navigation to a large amount of image data while the LVQ rectifies the inconsistency in topological data representation between different layers.
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Liu, Y., Takatsuka, M. (2009). Interactive Hierarchical SOM for Image Retrieval Visualization. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10684-2_95
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DOI: https://doi.org/10.1007/978-3-642-10684-2_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10682-8
Online ISBN: 978-3-642-10684-2
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