Abstract
This paper presents a technique based on statistical and neural feature extractor, classifier and retrieval for real world texture images. The paper is presented into two stages, texture image pre-processing includes downloading images, normalizing into specific rows and columns, forming non-overlapping windows and extracting statistical features. Co-occrance based statistical technique is used for extracting four prominent texture features from an image. Stage two includes, feeding of these parameters to Multi-Layer Perceptron (MLP) as input and output. Hidden layer output was treated as characteristics of the patterns and fed to classifier to classify into six different classes. Graphical user interface was designed to pose a query of texture pattern and retrieval results are shown.
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© 2012 Springer-Verlag Berlin Heidelberg
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Kulkarni, S., Kulkarni, P. (2012). Texture Feature Extraction and Classification by Combining Statistical and Neural Based Technique for Efficient CBIR. In: Kim, Th., Kang, JJ., Grosky, W.I., Arslan, T., Pissinou, N. (eds) Computer Applications for Bio-technology, Multimedia, and Ubiquitous City. BSBT MulGraB IUrC 2012 2012 2012. Communications in Computer and Information Science, vol 353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35521-9_14
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DOI: https://doi.org/10.1007/978-3-642-35521-9_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35520-2
Online ISBN: 978-3-642-35521-9
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