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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2013

01.02.2013

Image Compression and Video Segmentation Using Hierarchical Self-Organization

verfasst von: Esteban J. Palomo, Enrique Domínguez, Rafael M. Luque-Baena, José Muñoz

Erschienen in: Neural Processing Letters | Ausgabe 1/2013

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Abstract

Both image compression based on color quantization and image segmentation are two typical tasks in the field of image processing. Several techniques based on splitting algorithms or cluster analyses have been proposed in the literature. Self-organizing maps have been also applied to these problems, although with some limitations due to the fixed network architecture and the lack of representation in hierarchical relations among data. In this paper, both problems are addressed using growing hierarchical self-organizing models. An advantage of these models is due to the hierarchical architecture, which is more flexible in the adaptation process to input data, reflecting inherent hierarchical relations among data. Comparative results are provided for image compression and image segmentation. Experimental results show that the proposed approach is promising for image processing, and the powerful of the hierarchical information provided by the proposed model.
Vorheriger Artikel A Competitive Neural Network for Multiple Object Tracking in Video Sequence Analysis
Nächster Artikel Segmentation of Different Skin Colors with Different Lighting Conditions by Combining Graph Cuts Algorithm with Probability Neural Network Classification, and its Application

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Fußnoten
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Metadaten
Titel
Image Compression and Video Segmentation Using Hierarchical Self-Organization
verfasst von
Esteban J. Palomo
Enrique Domínguez
Rafael M. Luque-Baena
José Muñoz
Publikationsdatum
01.02.2013
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2013
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-012-9266-5

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