Label cost optimization proposes a new improvement in label cost function, improving existing moves of α-expansion algorithm and introducing some new moves for this algorithm. In order to study the performance comparison, different metrics of energy minimization has been considered. An appropriate comparison has been drawn among proposed technique i.e. fast approximation algorithm and previous well known techniques. The objective is to effectively optimize energies so that satisfactory image segmentation can be obtained (represented with different labels respective to different objects). New combinatorial optimization algorithm have been proposed which shows promising experimental results with the new moves, which we believe could be used in any context where α -expansions are currently employed.
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