Saliency Detection in Eye Gaze Prediction by Using Deep Learning

Salient object detection attracted the attention of researchers in various fields, so that it is used in many applications of the visual machine, such as object detection and tracking. Most of the work in this area is based on bottom-top saliency models and use low-level features to extract the final saliency map that these works do not have a significant accuracy in salient object detection. On the other hand, top-down visual models are used for the specific applications. In this paper, a different method has been proposed to extract the salient object map that uses low-up and top-down attributes for extracting the salient and it is based on the learning process. The simultaneous selection of these features makes the proposed algorithm for various applications and increases the accuracy of the salient object detection. The learning process is performed by using the and Convolutional Neural Network (CNN) structure. After the decomposition of image to its super pixels, different features of image are extracted. Then, the extracted features are normalized to have zero mean and unit variance, and CNN is used to train the features. The accuracy of the proposed method is improved by using of the 8 salient and CNN. The performance of the proposed method has been compared to twenty method by applying four new databases including MSRA-100, ECSSD, MSRA-10K and Paskal-S. The proposed method provides better results compared to the other methods in respect to prediction of salient object.