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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15907
Title: Combining global with local texture information for image retrieval applications
Authors: Montoya Zegarra, Javier
Beeck, Jan
Jerônimo Leite, Neucimar
da Silva Torres, Ricardo
Falcao, Alexandre
Keywords: Image retrieval;Comparative evaluations;Data sets;Descriptor;Feature representations;Image database;Local binary pattern operators;Local textures;Micro-patterns;Multi-resolution properties;Retrieval accuracies;Retrieval applications;Search and retrievals;Steerable pyramids;Texture extractions;Textured images;Textures
Issue Date: 2008
Publisher: Scopus
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-62949238164&doi=10.1109%2fISM.2008.113&partnerID=40&md5=efc691f0ccfff0707d0fca722755b81d
Abstract: This paper proposes a new texture descriptor to guide the search and retrieval in image databases. It extracts rich information from global and local primitives of textured images. At a higher level, the global macro-features in textured images are characterized by exploiting the multi-resolution properties of the Steerable Pyramid Decomposition. By doing this, the global texture configurations are highlighted. At afiner level, the local arrangements of texture micro-patterns are encoded by the Local Binary Pattern operator. Experiments were carried out on the standard Vistex dataset aiming to compare our desriptors against popular texture extraction methods with regard to their retrieval accuracies. The comparative evaluations allowed us to show the superior descriptive properties of our feature representation methods. © 2008 IEEE.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15907
ISBN: urn:isbn:9780769534541
Appears in Collections:Artículos - Ciencia de la computación

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