Abstract: One of the commonly-used detection methods in shape matching is the sliding window,in which multiple objects,different in size and position,can be detected．The detection performance is generally measured by detection rate and false positive rate．The two parameters in sliding window detection method,sliding step and the scale step are empirically selected for high detection rate and low false positive rate．However,those two factors can be formulated as a typical two-objective optimization problem,while the empirical selection shows no consideration over decision-makers’ different preferences regarding detection rate and false positive rate．Given the fact that decision makers’ positive pre-ferences are represented by high detection rate and low false positive rate,and negative preferences,by low detection rate and high false positive rate,the paper introduced the bipolar control strategy and then proposed a new way to optimize the sliding window parameters based on Bipolar Preference Multi-objective Particle Swarm Optimization (BPMOPSO)．The new method was applied in the detection experiment on Leeds Cows image datasets．The experiment results show that the performance of the new method is largely improved,i.e.,the false positive rate declines sharply and the detection rate improves significantly,and that the efficiency of the algorithm ameliorates considerably as well.

Key words: Sliding window,Multi-objective algorithm,Bipolar preferences,Parameter optimization