Texture Image Classification Using Extended 2D HLAC Features

Motofumi Suzuki
The Open University of Japan, Japan

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Ingår i: KEER2014. Proceedings of the 5th Kanesi Engineering and Emotion Research; International Conference; Linköping; Sweden; June 11-13

Linköping Electronic Conference Proceedings 100:91, s. 1093-1102

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Publicerad: 2014-06-11

ISBN: 978-91-7519-276-5

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


HLAC (Higher Order Local Autocorrelation) features are popular image descriptors that have been used for various image-processing applications since the 1980s. Examples of the application of the HLAC features include KANSEI retrievals and subjective retrievals of 2D image databases. In this paper; standard HLAC masks are extended for computing a massive number of features. Typical HLAC features are computed by applying 25 masks to a binary image; whereas our Ext-HLAC features are computed by applying 16;241;567 masks. Since there are a high number of mask combinations; we have developed Ext-HLAC mask generation software programs. Ext-HLAC masks were tested by using 2D benchmark image database sets. For each image; the pattern features were extracted by applying Ext-HLAC masks; and the pattern features were analyzed by a k-NN based approach. Our preliminary experiments show high classification rates for certain image databases.


HLAC; Ext-HLAC; pattern feature; k-NN; image classification


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