At present, image recognition methods have been widely used in the research on measuring particle mixing and segregation in rotating drums. However, currently commonly used methods tend to study mixing and segregation from the perspective of the radial surface of the particles, but rarely from the perspective of the axial surface. Whether the data obtained from the particle surface (radial segregation or axial segregation) can reflect the degree of mixing and segregation of the entire particle system (overall segregation) is still a question worth studying. In this work, an image analysis method based on stacked images to measure the degree of surface segregation is proposed. The drum length to diameter ratio (L/D) and particle filling rate are used as operating parameters. And the discrete element method (DEM) verified by experiments has studied the difference between the surface segregation and the overall segregation of the binary particle groups (different particle sizes) in the rotating drum. This paper compares the qualitative characteristics (particle segregation characteristic patterns) and quantitative characteristics (particle segregation degree) of surface and overall mixing and segregation in detail. The results show that for short drums, radial segregation index can be used to quantify the overall degree of segregation. While for long drums, axial segregation index can be used to quantify the overall degree of segregation. (C) 2020 Elsevier B.V. All rights reserved.