The multiple-point variogram of images for robust texture classification
2016 (English)In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE , 2016, 1303-1307 p.Conference paper (Refereed)
Most texture analysis techniques require training data to perform classification or retrieval of images. In many practical situations, the amount of data representing different texture classes can be too limited to satisfy the training of a reliable classifier. Therefore, finding an effective feature of texture is very useful to cope with a variety of applications. This paper presents the extension of the two-point variogram to multiple-point variogram of images for texture feature extraction, which is also robust to noise and computationally economic. The matching of the variogram functions for pattern classification can be enhanced with the use of a spectral distortion measure without the requirement of training data. Experimental results and comparison with other methods, which require training data, suggest the usefulness of the proposed approach.
Place, publisher, year, edition, pages
IEEE , 2016. 1303-1307 p.
, Speech and Signal Processing, ISSN 2379-190X
Distortion, Distortion measurement, Training data, Training, Feature extraction, Robustness, Gaussian noise
Medical Image Processing
IdentifiersURN: urn:nbn:se:liu:diva-127388DOI: 10.1109/ICASSP.2016.7471887OAI: oai:DiVA.org:liu-127388DiVA: diva2:922933
The 41st IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP 2016), March 20-25, Shanghai, China