Unsupervised Quality Based Image Segmentation

This paper presents an efficient technique for unsupervised segmentation of textured images that aims at incorporating the advantages of supervision for discriminating texture patterns. First, a pattern discovery stage that relies on a clustering algorithm is utilized for determining the outcome of a multichannel Gabor filter bank. Then, a supervised pixel vectors associated with those patterns is used to classify every image pixel into one of the soug thus yielding the final segmentation. Multi during pixel classification in order to improve accuracy both inside and near boundaries of regions of homogeneous texture.. The proposed technique is compared with alternative segmentation approache