@article {590,
	title = {Rapid extraction of image texture by co-occurrence using a hybrid data structure},
	journal = {Computers \& Geosciences},
	volume = {28},
	year = {2002},
	pages = {763 - 774},
	abstract = {<p>Calculation of co-occurrence probabilities is a popular method for determining texture features within remotely sensed digital imagery. Typically, the co-occurrence features are calculated by using a grey level co-occurrence matrix (GLCM) to store the co-occurring probabilities. Statistics are applied to the probabilities in the GLCM to generate the texture features. This method is computationally intensive since the matrix is usually sparse leading to many unnecessary calculations involving zero probabilities when applying the statistics. An improvement on the GLCM method is to utilize a grey level co-occurrence linked list (GLCLL) to store only the non-zero co-occurring probabilities. The GLCLL suffers since, to achieve preferred computational speeds, the list should be sorted. An improvement on the GLCLL is to utilize a grey level co-occurrence hybrid structure (GLCHS) based on an integrated hash table and linked list approach. Texture features obtained using this technique are identical to those obtained using the GLCM and GLCLL. The GLCHS method is implemented using the C language in a Unix environment. Based on a Brodatz test image, the GLCHS method is demonstrated to be a superior technique when compared across various window sizes and grey level quantizations. The GLCHS method required, on average, 33.4\% ([sigma]=3.08\%) of the computational time required by the GLCLL. Significant computational gains are made using the GLCHS method.</p>
},
	keywords = {Co-occurrence probabilities, Hash table, linked list, remote sensing imagery, texture features},
	issn = {0098-3004},
	doi = {http://dx.doi.org/10.1016/S0098-3004(01)00108-X},
	url = {http://www.sciencedirect.com/science/article/pii/S009830040100108X},
	author = {D A. Clausi and Y Zhao}
}
