TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

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

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivut943–957
Sivumäärä15
JulkaisuNeural Computing and Applications
Vuosikerta29
Numero4
Varhainen verkossa julkaisun päivämäärä29 heinäkuuta 2016
DOI - pysyväislinkit
TilaJulkaistu - helmikuuta 2018
OKM-julkaisutyyppiA1 Alkuperäisartikkeli

Tiivistelmä

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.

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