A neural model combining attentional orienting to object recognition: Preliminary explorations on the interplay between where and what

We propose a model of primate vision that integrates both an attentional orienting ("where") pathway and an object recognition ("what") pathway. The fast visual attention front-end rapidly selects the few most conspicuous image locations, and the slower object recognition back-end identifies objects at the selected locations. The model is applied to classical visual search tasks, consisting of finding a specific target among an array of distracting visual patterns (e.g., a circle among many squares). The encouraging results obtained, in which substantial speedup is achieved by the combined attention-recognition model while maintaining good recognition performance compared to an exhaustive search, suggest that the biologically-inspired architecture proposed represents an efficient solution to the difficult problem of rapid scene analysis.