Combining global with local texture information for image retrieval applications

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Date
2008
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Scopus
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.
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