Rudak, EwaRynkiewicz, FilipDaszuta, MarcinSturgulewski, ŁukaszLazarek, Jagoda2021-10-262021-10-262021Rudak E., Rynkiewicz F., Daszuta M., Sturgulewski Ł., Lazarek J., Prediction of Natural Image Saliency for Synthetic Images. W: TEWI 2021 (Technology, Education, Knowledge, Innovation), Wojciechowski A. (Ed.), Napieralski P. (Ed.), Lipiński P. (Ed.)., Seria: Monografie PŁ;Nr 2378, Wydawnictwo Politechniki Łódzkiej, Łódź 2021, s. 155-169, ISBN 978-83-66741-10-2, DOI 10.34658/9788366741102.11.978-83-66741-10-2http://hdl.handle.net/11652/4031https://doi.org/10.34658/9788366741102.11Numerous saliency models are being developed with the use ofneural networks and are capable of combining various features and predicting the saliency values with great results. In fact, it might be difficult to replace the possibilities of artificial intelligence applied to algorithms responsible for predicting saliency. However, the low-level features are still important and should not be removed completely from new saliency models. This work shows that carefully chosen and integrated features, including a deep learning based one, can be used for saliency prediction. The integration is obtained by using Multiple Kernel Learning. This solution is quite effective, as compared to a few other models tested on the same dataset.enFair use conditionDla wszystkich w zakresie dozwolonego użytkucomputer gamesartificial intelligenceimage saliencyHuman Visual Attentiongry komputerowesztuczna inteligencjaistotność obrazuuwaga wzrokowa człowiekaRozdział książkiLUT LicenseLicencja PŁ10.34658/9788366741102.11