Colour clustering is one of the colour reduction methods applicable for segmentation. Nevertheless, it has not been an appropriate method cause of colour and texture variety of outdoor image. Hence, customizing segmentation is necessary for outdoor image which can improve the quality of computer vision system to a large extent by recognizing the objects. This study focuses on customizing the colour clustering methods for segmentation and object recognition in the outdoor images by utilizing a

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... lti-phase procedure through a multi-resolution platform, called Colour Cluster Elimination (CCE), where, the primary colour clusters are detected and then gradually eliminated, allowing the smaller clusters to emerge in much clearer versions of the image. Afterward, each colour region is examined to identify the adjacent homochromatic objects with different textures. A new texture feature: Texture Sewn Response (TSR) has been introduced to indicate the texture strength in different direction/resolutions. The strength has been shown by combining the energies of the detail images applying the Gabor filters bank. Finally, the proposed method has been applied for evaluating on outdoor images dataset namely BSDS500 and the results have been compared to PRI, NPR, and GCE statistical metrics of the latest segmentation methods which demonstrated that the proposed method has a satisfactory performance for the segmentation of the outdoor scenes.