Predicting human gaze with multi-level information

Eye fixation models, which try to quantitatively predict human eye attended areas in visual fields, have received increasing interest in recent years. In this paper, a novel framework is proposed for the detection of eye fixations. First, a multi-channel detection module, which extracts information of color contrast, salient object proposals and center bias from input image, is conducted to introduce various useful information into the subsequent fixations detection. In salient object detection channel, we employ the multi-instance learning (MIL) algorithm to determine which object proposal can attract attention, which avoids the fuzzyness of positive sample selection. Second, an adaptive weighted fusion method achieved by deep learning framework is proposed to fuse the multi-level information (i.e., contrast, objective, center bias) together for the detection of fixations, so that the integration of information between each level becomes more scientific. Finally, the detection result is optimized by embedding semantic information. Experimental results show that the algorithm has achieved competitive results in MIT1003, MIT300 and Toronto120 dataset.