Reduced multidimensional co-occurrence histograms in texture classification
- 1 January 1998
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 20 (1) , 90-94
- https://doi.org/10.1109/34.655653
Abstract
Textures are frequently described using co-occurrence histograms of gray levels at two pixels in a given relative position. Analysis of several co-occurring pixel values may benefit texture description but is impeded by the exponential growth of histogram size. To make use of multidimensional histograms, we have developed methods for their reduction. The method described here uses linear compression, dimension optimization, and vector quantization. Experiments with natural textures showed that multidimensional histograms reduced with the new method provided higher classification accuracies than the channel histograms and the wavelet packet signatures. The new method was significantly faster than our previous one.Keywords
This publication has 12 references indexed in Scilit:
- Co-occurrence map: Quantizing multidimensional texture histogramsPattern Recognition Letters, 1996
- Multidimensional Co-occurrence Matrices for Object Recognition and MatchingGraphical Models and Image Processing, 1996
- Self-Organizing MapsPublished by Springer Nature ,1995
- Texture classification by wavelet packet signaturesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1993
- Vector quantization for texture classificationIEEE Transactions on Systems, Man, and Cybernetics, 1993
- Unsupervised textural classification of images using the texture spectrumPattern Recognition, 1992
- Vector Quantization and Signal CompressionPublished by Springer Nature ,1992
- Local linear transforms for texture measurementsSignal Processing, 1986
- Vector quantizationIEEE ASSP Magazine, 1984
- Statistical and structural approaches to textureProceedings of the IEEE, 1979