Improved 3D Co-Occurrence Matrix for Texture Description and Classification

This paper proposes an improved feature extraction method for volumetric texture classification. Our approach consists in the computation of 3D co-occurrence matrices built using both the image intensity and the gradient image information. The feature vector represents the concatenation of the Haral...

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Bibliographic Details
Main Authors: Barburiceanu, Stefania, Terebes, Romulus, Meza, Serban
Format: Conference Proceeding
Language:English
Subjects:
3D co-occurrence matrix
3D feature extraction
Azimuth
Entropy
Feature extraction
GLCM
Image edge detection
Quantization (signal)
Three-dimensional displays
Two dimensional displays
volumetric texture classification
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Summary:This paper proposes an improved feature extraction method for volumetric texture classification. Our approach consists in the computation of 3D co-occurrence matrices built using both the image intensity and the gradient image information. The feature vector represents the concatenation of the Haralick second-order statistics and the proposed gradient-based and orientation-based indicators. The results obtained on a public synthetic 3D texture database show that the proposed technique is more discriminative and brings improvements in the classification performance when compared to recent 3D and 2D texture descriptors.
ISSN:2475-7861
DOI:10.1109/ISETC50328.2020.9301123