The analysis of color and texture has a long history in image analysis and computer vision. These two properties are often considered as independent, even though they are strongly related in images of natural objects and materials. Correlation between color and texture information is especially relevant in the case of variable illumination, a condition that has a crucial impact on the effectiveness of most visual descriptors. We propose an ensemble of hand-crafted image descriptors designed to capture different aspects of color textures. We show that the use of these descriptors in a multiple classifiers framework makes it possible to achieve a very high classification accuracy in classifying texture images acquired under different lighting conditions. A powerful alternative to hand-crafted descriptors is represented by features obtained with deep learning methods. We also show how the proposed combining strategy hand-crafted and convolutional neural networks features can be used together to further improve the classification accuracy. Experimental results on a food database (raw food texture) demonstrate the effectiveness of the proposed strategy.

CUSANO, C., NAPOLETANO, P., & SCHETTINI, R. (2016). Combining multiple features for color texture classification. JOURNAL OF ELECTRONIC IMAGING, 25(6), 1-9 [10.1117/1.JEI.25.6.061410].

Combining multiple features for color texture classification

CUSANO, CLAUDIO
Primo
;
NAPOLETANO, PAOLO
;
SCHETTINI, RAIMONDO
Ultimo
2016

Abstract

The analysis of color and texture has a long history in image analysis and computer vision. These two properties are often considered as independent, even though they are strongly related in images of natural objects and materials. Correlation between color and texture information is especially relevant in the case of variable illumination, a condition that has a crucial impact on the effectiveness of most visual descriptors. We propose an ensemble of hand-crafted image descriptors designed to capture different aspects of color textures. We show that the use of these descriptors in a multiple classifiers framework makes it possible to achieve a very high classification accuracy in classifying texture images acquired under different lighting conditions. A powerful alternative to hand-crafted descriptors is represented by features obtained with deep learning methods. We also show how the proposed combining strategy hand-crafted and convolutional neural networks features can be used together to further improve the classification accuracy. Experimental results on a food database (raw food texture) demonstrate the effectiveness of the proposed strategy.
Articolo in rivista - Articolo scientifico
color texture classification; color texture database; color texture features; ensemble of classifiers;
color texture classification; color texture database; color texture features; ensemble of classifiers; Electrical and Electronic Engineering; Computer Science Applications1707 Computer Vision and Pattern Recognition; Atomic and Molecular Physics, and Optics
English
1
9
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CUSANO, C., NAPOLETANO, P., & SCHETTINI, R. (2016). Combining multiple features for color texture classification. JOURNAL OF ELECTRONIC IMAGING, 25(6), 1-9 [10.1117/1.JEI.25.6.061410].
Cusano, C; Napoletano, P; Schettini, R
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/133327
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