Efficient Visual Search using Retinotopic convolutional neural networks

Foveal vision, a trait shared by many animals including primates, makes an important contribution to the performance of the visual system. However, it has been largely overlooked in machine learning applications. This study investigates whether retinotopic mapping, an essential component of foveal vision, can improve image categorisation and localisation performance when integrated with deep convolutional neural networks (CNNs). A retinotopic map is used as input to standard convolutional neural networks. These networks are then retrained on a visual classification task using the Imagenet dataset. Surprisingly, despite the loss of information due to foveal deformation, our re-trained networks show classification performance similar to the state of the art. In addition, during the test phase, the network showed an increased ability to detect regions of interest in the image with respect to the object predicted by the classifier. In other words, the network can analyse the entire image to find the position that best corresponds to the object being searched for. This visual search mechanism, a typical feature of the human visual system, is absent in typical CNNs. These results suggest that retinotopic mapping may play a key role in the design of visual recognition algorithms, whose ability to resist zooming and rotation may be valuable in an open environment.