An ordinal co-occurrence matrix framework for texture retrieval

Authors:
Mari Partio;Bogdan Cramariuc;Moncef Gabbouj
Affiliations:
Institute of Signal Processing, Tampere University of Technology, Tampere, Finland;Tampere eScience Applications Center, Tampere, Finland;Institute of Signal Processing, Tampere University of Technology, Tampere, Finland
Venue:
Journal on Image and Video Processing
Year:
2007

Citing 3
Cited 2

Texture Features for Browsing and Retrieval of Image Data

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Classification Using N-Tuple Pattern Recognition

ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472

Perceptual color descriptor based on spatial distribution: A top-down approach

Image and Vision Computing
Classification and retrieval on macroinvertebrate image databases

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a novel ordinal co-occurrence matrix framework for the purpose of content-based texture retrieval. Several particularizations of the framework will be derived and tested for retrieval purposes. Features obtained using the framework represent the occurrence frequency of certain ordinal relationships at different distances and orientations. In the ordinal co-occurrence matrix framework, the actual pixel values do not affect the features, instead, the ordinal relationships between the pixels are taken into account. Therefore, the derived features are invariant to monotonic gray-level changes in the pixel values and can thus be applied to textures which may be obtained, for example, under different illumination conditions. Described ordinal co-occurrence matrix approaches are tested and compared against other well-known ordinal and nonordinal methods.