Patent ID: 8636361

Claim:
A learning-based visual attention prediction system, comprising: a fixation data collection unit configured to receive a plurality of training video sequences which comprises a plurality of training frames and configured to detect a plurality of fixation points gazed in each of the plurality of training frames of the plurality of training video sequences and collect the fixation points to generate a fixation map for each of the plurality of training frames; and a fixation density generator, coupled to the fixation data collection unit, configured to transform each of the fixation maps into a fixation density map which carries a fixation density value for every pixel in the corresponding training frame; a feature extraction unit configured to receive a test video sequence which comprises a plurality of test frames and generate at least one tested feature map for each test frame based on at least one feature information, wherein the feature information comprises color, motion, orientation, or face; a regression model, having a correlation relationship between fixation density and the feature information, configured to map the at least one tested feature map into a saliency map based on the correlation relationship, wherein the saliency map indicates fixation strength of the test frame based on the fixation density; and a training unit configured to train the regression model to learn the correlation relationship between the fixation density and the feature information; wherein the feature extraction unit further receives the training video sequences and generates at least one training feature map for each of the plurality of training frames of the training video sequences based on the at least one feature information, and the training unit trains the regression model according to the fixation density maps and the training feature map; and wherein the motion feature information denotes a relative motion of each pixel or block in an image and the orientation feature information is formed from a contrast of orientation obtained by computing a difference of two local orientation images.