Abstract:

In order to more objectively and accurately quantify the relationship between cultural characteristics and imagery, a cultural image prediction model integrating the hybrid gray wolf optimization algorithm (DE-GWO) and support vector regression (SVR) was proposed. First, the image space of the cultural characteristics of the Xiangtangshan Grottoes statues was constructed based on multiple sets of image vocabulary, and cultural image cognition experiments were conducted using eye tracking technology. In doing so, with the subjects' physiological cognition data obtained, the one-way analysis of variance was carried out, and then the eye movement index parameter data set of the cultural image prediction model was obtained. Secondly, the differential evolution strategy based on the DE algorithm was introduced to make up for the problem of GWO search process stagnation. Thirdly, the improved GWO algorithm was used to change the parameter C of the SVR model and g for optimization. Finally, the constructed DE-GWO-SVR model was utilized to realize the prediction of cultural image cognition. In order to further prove the generalization of the constructed model, five models including BP, ABC-SVR, and DT were involved to conduct comparative experiments. The results show that the proposed model could achieve a better prediction effect on cultural image cognition.

Key words: hybrid gray wolf optimization algorithm, support vector regression, eye tracking technology, cultural image prediction