Swarm optimization based texture classification in extreme scale variations

Texture analysis has remained a remarkably trendsetting and productive field of research in the last two decades. There been much progress, but impact illumination changes on automated texture classification segmentation gained very less focus. Research work carried out identification frequently focuses textures with intraclass including illumination, rotation, viewpoint small scale variations. Consequently, variations owing to modifications constitute among ones that are difficult manage. In this work, as first step, due vast is studied. order deal problem, solution introduced then reduced based predominant patterns texture. Inspired by challenges imposed issue, novel swarm intelligence approach known Ant Colony Optimization (ACO) algorithm for modifying components hidden layers used during network training, extraction more useful semantic patterns.