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.

EMLNET:An Expandable MultiLayer NETwork for Saliency Prediction Abstract   Existing advanced saliency systems have been proposed using Convolutional Neural Networks (CNNs), the prior knowledge of objectness is crucial for saliency detection. However, we show that the use of objectness may also limit the power of CNNs on the images which do not contain a salient object. Besides, one previous study has shown applying a deeper CNN model may not improve the performance, the reason behind could be due to the limited training data. In this work, we aim at investigating the effect of prior knowledge from other domains and proposing a multimodality system. Our work shows the depth of the model still plays an important role in saliency detection when the training data is large enough. To do this in a sophisticated manner can be complex, and also result in unwieldy networks or produce competing objectives that are hard to balance. For the scalability, our multimodality system is trained in an almost endtoend piecewise fashion. The encoder and decoder components are separately trained to deal with complexity tied to the computational paradigm and required space. Therefore, our system can be easily extended to include a variety of prior knowledge for saliency detection. Besides, we also propose a combined saliency loss based on modifications of Pearson correlation and normalized scanpath saliency. Our experiment shows the combined loss can train a CNN model more comprehensively for saliency detection. We denote our expandable multilayer network as EMLNET and our method achieves the stateoftheart results on the public saliency benchmarks, SALICON, MIT300 and CAT2000.