Model of top-down / bottom-up visual attention for location of salient objects in specific domains

There are several real situations in which it is useful to have a system able to detect a specific target or a salient object and its localization in a given image in autonomous way. To guide the attention based on known characteristics of an object and primitive information of the image is not a trivial task for visual attention. Several works about visual attention have been developed, which focused in bottom-up or top-down in an isolate manner. We propose in this work a model of visual attention combining characteristics bottom-up and top-down. The proposed model is composed of four components: the training and recognition of known objects, the object segmentation of the input image, the self-organizing of information top-down and bottom-up in a single map and a network of neurons with excitatory connections and inhibitory connections to generate the map of salient attribute for the location of salient objects. Thanks to this combination it was possible to detect, to identify and to locate the salient objects of the image. Several tests have been applied to synthetic images to verify the viability of the model as a mechanism of selection of objects as a part of a visual attention system. The results demonstrate the effectiveness of the model.