A precise analysis of an entire image is computationally wasteful if one is interested in finding a target object located in a subregion of the image. A useful "attention strategy" can reduce the overall computation by carrying out fast but approximate image measurements and using their results to suggest a promising subregion. The paper proposes a maximum-likelihood attention mechanism that does this. The attention mechanism recognizes that objects are made of parts and that parts have different features. It works by proposing object part and image feature pairings which have the highest likelihood of coming from the target. The exact calculation of the likelihood as well as approximations are provided. The attention mechanism is adaptive, that is, its behavior adapts to the statistics of the image features. Experimental results suggest that, on average, the attention mechanism evaluates less than 2 percent of all part-feature pairs before selecting the actual object, showing a significant reduction in the complexity of visual search.
['Hemant D. Tagare', 'Kentaro Toyama', 'Jonathan G. Wang']
A maximum-likelihood strategy for directing attention during visual search