Classification of ordered texture images using regression modelling and granulometric features

Mahmuda Khatun, Alison Gray, Stephen Marshall

Research output: Contribution to conferencePaper

2 Citations (Scopus)
267 Downloads (Pure)


Structural information available from the granulometry of an image has been used widely in image texture analysis and classification. In this paper we present a method for classifying texture images which follow an intrinsic ordering
of textures, using polynomial regression to express granulometric moments as a function of class label. Separate models are built for each individual moment and combined for back-prediction of the class label of a new image. The methodology was developed on synthetic images of evolving textures and tested using real images of 8 different grades of cut-tear-curl black tea leaves.
For comparison, grey level co-occurrence (GLCM) based features were also computed, and both feature types were used in a range of classifiers including the regression approach. Experimental results demonstrate the superiority of the granulometric moments over GLCM-based features for classifying these tea images.
Original languageEnglish
Number of pages6
Publication statusPublished - 9 Sept 2011
EventIrish Machine Vision and Image Processing Conference - Dublin, Ireland
Duration: 8 Sept 20119 Sept 2011


ConferenceIrish Machine Vision and Image Processing Conference


  • granulometry
  • structuring
  • pattern spectrum


Dive into the research topics of 'Classification of ordered texture images using regression modelling and granulometric features'. Together they form a unique fingerprint.

Cite this