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Image segmentation is the process of subdividing an image into multiple regions. In other words, a label could be assigned to each pixel in the image such that pixels with the same label share a particular characteristic. Especially, in this work, each image region is assigned with a unique label representing a texture class. It is always true that low level image analysis that is based on classified image regions is more expressive than the one based on individual pixels. Hence, image segmentation is found by many researchers to be the most significant task for precise image interpretation (Sezgin, 2004).
Most of the conventional methods being adopted for image segmentation (Roy, 2014; Lu, 2007) depend on spectral features of an image, and they may lead to wrong segmentation, particularly in case of remotely sensed images. Therefore, image spatial features are preferred over spectral features for segmentation. It is noticed that texture, one of the spatial features, is contained in most of the images of natural scenes, ragged or worn surfaces of many objects and particularly in remote sensing images. Hence, the texture features are expected to provide a significant contribution for image segmentation (Haralick, 1973; Chen, 1995; Ojala, 2001; Li, 2003; Liu, 2006; Ranjan, 2014). It is also found that texture feature based segmentation is found to be more appropriate in many of the computer vision applications such as remote sensing (Chakraborty, 2012; Tsai, 2005; Roy, 2014; Cheng, 2013), defect detection in industrial applications (Xie, 2008; Iyer, 2014), medical imaging (Hajar Danesh, 2014) and content based image retrieval (Yue, 2011; Zhang, 2000).