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Novel CBIR System Based on Ripplet Transform Using Interactive Neuro-Fuzzy Technique
Chowdhury, Manish (Indian Statistical Institute. Machine Intelligence Unit)
Das, Sudeb (Indian Statistical Institute. Machine Intelligence Unit)
Kumar Kundu, Malay (Indian Statistical Institute. Machine Intelligence Unit)

Fecha: 2012
Resumen: Content Based Image Retrieval (CBIR) system is an emerging research area in effective digital data management and retrieval paradigm. In this article, a novel CBIR system based on a new Multiscale Geometric Analysis (MGA)-tool, called Ripplet Transform Type-I (RT) is presented. To improve the retrieval result and to reduce the computational complexity, the proposed scheme utilizes a Neural Network (NN) based classifier for image pre-classification, similarity matching using Manhattan distance measure and relevance feedback mechanism (RFM) using fuzzy entropy based feature evaluation technique. Extensive experiments were carried out to evaluate the effectiveness of the proposed technique. The performance of the proposed CBIR system is evaluated using a 2 £ 5-fold cross validation followed by a statistical analysis. The experimental results suggest that the proposed system based on RT, performs better than many existing CBIR schemes based on other transforms, and the difference is statistically significant.
Derechos: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Lengua: Anglès
Documento: article ; recerca ; publishedVersion
Materia: Ripplet Transform ; Relevance Feedback ; Content Based Image Retrieval ; Artificial Neural Network ; Multilayer Perceptron ; Fuzzy
Publicado en: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 11, Núm. 1 (2012) , p. 1-13, ISSN 1577-5097

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DOI: 10.5565/rev/elcvia.445

13 p, 918.0 KB

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