Existing color editing algorithms enable users to edit the colors in an image according to their own aesthetics.Unlike artists who have an accurate grasp of color,ordinary users are inexperienced in color selection an...Existing color editing algorithms enable users to edit the colors in an image according to their own aesthetics.Unlike artists who have an accurate grasp of color,ordinary users are inexperienced in color selection and matching,and allowing non-professional users to edit colors arbitrarily may lead to unrealistic editing results.To address this issue,we introduce a palette-based approach for realistic object-level image recoloring.Our data-driven approach consists of an offline learning part that learns the color distributions for different objects in the real world,and an online recoloring part that first recognizes the object category,and then recommends appropriate realistic candidate colors learned in the offline step for that category.We also provide an intuitive user interface for efficient color manipulation.After color selection,image matting is performed to ensure smoothness of the object boundary.Comprehensive evaluation on various color editing examples demonstrates that our approach outperforms existing state-of-the-art color editing algorithms.展开更多
基金supported by National Natural Science Foundation of China(Grant Nos.61972216 and 62111530097)NSF of Tianjin City(Grant Nos.18JCYBJC41300 and 18ZXZNGX00110).
文摘Existing color editing algorithms enable users to edit the colors in an image according to their own aesthetics.Unlike artists who have an accurate grasp of color,ordinary users are inexperienced in color selection and matching,and allowing non-professional users to edit colors arbitrarily may lead to unrealistic editing results.To address this issue,we introduce a palette-based approach for realistic object-level image recoloring.Our data-driven approach consists of an offline learning part that learns the color distributions for different objects in the real world,and an online recoloring part that first recognizes the object category,and then recommends appropriate realistic candidate colors learned in the offline step for that category.We also provide an intuitive user interface for efficient color manipulation.After color selection,image matting is performed to ensure smoothness of the object boundary.Comprehensive evaluation on various color editing examples demonstrates that our approach outperforms existing state-of-the-art color editing algorithms.
