Ensemble of Receptive Fields for Training CentralFocused Convolutional Neural Networks Translation is a data augmentation method widely used in the image classification tasks. We analyze the mechanism of translation and discover that the central area of the images is more likely to be selected as convolutional neural networkxe2x80x99s input. Inspired by the structure of human retina, we propose the hypothesis that the central area of the image contains more significant information than the marginal one. Comprehensive experiments are presented to prove that hypothesis and reach the conclusion that the receptive field is nonuniform. The central part of the image that is always selected by translation is called the focused area. Motivated by the demand to take use of different focused area and thus take use of different receptive fields, we propose a novel training mechanism that integrate different focused areas in one training process. Our method consists of several stages, each has its own focused area and learning rate, and achieves considerable result in the experiments. We call the integration of focused areas the ensemble of receptive fields.

Salient Object Detection based on CNN Fusion of Two Types of Saliency Models Salient object detection has attracted significant attention, and recently many heuristic computational models have been developed for object detection. In this paper, we introduced a new approach that is based on convolutional neural network fusion strategy to combine the saliency maps generated by highdimensional color transform and salient object detection integrating discriminative regional features methods. Our method is based on the observation of salient regions and has a distinctive role in removing background information, which in humanxe2x80x99s perspective is not required in the object detection task. The key contribution of this work is a pipeline of CNN based fusion method, which not only performs best but also generates the saliency maps closer to the ground truth. The experimental results show that our method demonstrates the effectiveness of the proposed pipeline while comparing it with other stateoftheart methods.