In this paper, we propose an object selective attention and perception system, which was implemented by integrating a specific object preferable attention model with an incremental object perception model. The object oriented attention model can selectively pay attention to the candidates of an object in natural scenes based on a bottom-up selective attention model in conjunction with a top-down biased attention mechanism for a specific object. A generative model based on an incremental Bayesian parameter estimation is considered in order to perceive arbitrary objects in the attended areas. Combining an object oriented attention model with general object perception model, the developed system cannot only pay attention to a specific target object but can also memorize the characteristics of task nonspecific objects in an incremental manner. Experimental results show that the developed system generates good performance in successfully focusing on the target objects as well as incrementally perceiving objects in natural scenes.
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