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Neural Computing and Applications

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29.07.2016 | Original Article

Feature synthesis for image classification and retrieval via one-against-all perceptrons

verfasst von: Jenni Raitoharju, Serkan Kiranyaz, Moncef Gabbouj

Erschienen in: Neural Computing and Applications | Ausgabe 4/2018

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Abstract

Most existing content-based image retrieval and classification systems rely on low-level features which are automatically extracted from images. However, often these features lack the discrimination power needed for accurate description of the image content, and hence, they may lead to a poor retrieval or classification performance. We propose a novel technique to improve low-level features which uses parallel one-against-all perceptrons to synthesize new features with a higher discrimination power which in turn leads to improved classification and retrieval results. The proposed method can be applied on any database and low-level features as long as some ground-truth information is available. The main merits of the proposed technique are its simplicity and faster computation compared to existing feature synthesis methods. Extensive simulation results show a significant improvement in the features’ discrimination power.

Vorheriger Artikel Multi-objective symbolic regression using long-term artificial neural network memory (LTANN-MEM) and neural symbolization algorithm (NSA)

Nächster Artikel A co-evolutionary design methodology for complex AGV system

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Titel: Feature synthesis for image classification and retrieval via one-against-all perceptrons
verfasst von: Jenni Raitoharju
Serkan Kiranyaz
Moncef Gabbouj
Publikationsdatum: 29.07.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 4/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2504-4

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