Rapid determination of co-occurrence texture features

TitleRapid determination of co-occurrence texture features
Publication TypeConference Paper
Year of Publication2001
AuthorsClausi, D. A., and Y. Zhao
Conference NameIEEE Geoscience and Remote Sensing Symposium
KeywordsBrodatz test image, co-occurrence features, co-occurrence texture feature, geophysical measurement technique, geophysical signal processing, geophysical techniques, grey level co-occurrence hybrid structure, grey level co-occurrence linked list, grey level co-occurrence matrix, image processing, image texture, integrated hash table, land surface, linked list, Remote Sensing, terrain mapping

Typically, the co-occurrence features for image processing are calculated-by using a grey level co-occurrence matrix (GLCM). This method is computationally intensive since the matrix is usually sparse leading to many unnecessary calculations involving zero probabilities. 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. This paper presents 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. 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% of the computational time ( sigma;=3.08%) required by the GLCLL. Significant computational gains are made using the GLCHS method