The invention belongs to the field of computer vision, and discloses a fixation point detection method based on local evaluation and global optimization. The method comprises the steps of extracting possible candidate targets in an image; then utilizing a supervised learning method to conduct local evaluation on the targets, wherein two evaluation methods are as follows, (1), grading significance of each proposals using image training SVM of a whole database; (2), utilizing a semi-coupled dictionary learning algorithm, reconstituting different SVM aiming at different images, and grading the proposals of the image with pertinence; after local evaluation, utilizing a proposal subset optimization algorithm to cluster proposals; finally conducting global optimization. According to fixation point detection method based on local evaluation and global optimization, aiming at the characteristics of different information catching the attention of human eyes, a model capable of seizing the information is designed, and the human eye gazing area in an image containing semantic information, an image containing objects, and an image which is complicated or does not contain the objects can be effectively detected.