In spite of the fact that a collection of various trees of four different seasons can be found from the internet. Nevertheless, it is definitely not an easy task for a photographer or a professional to take a picture of the same tree for different seasons. In this paper, we provide a comprehensive system to generate a new image of a different season from a given image by reflecting the seasonal characteristics. We construct a new color transfer approach through the HSV color space. We also create the generated season change more properly by identifying the foreground and background of the image. By knowing the foreground of a scene, the system ensures the color modification only apply to the main object which is detected. Once we make in-depth observations of a tree, we extract a tree characteristic from the particular flowers on these trees with different magnitudes and directions. Currently, we create a system that can deploy the flowers along particular paths by generating random scales and rotations. Edge detection and K-Means Clustering are used to find a reasonable place to grow the flowers. Additionally, this system can also be assisted with user interaction to mark the reasonable result. The collaboration between the system and the user would be essential to hint the foreground and background of the image and also to control the final result for an acceptable output.

In spite of the fact that a collection of various trees of four different seasons can be found from the internet. Nevertheless, it is definitely not an easy task for a photographer or a professional to take a picture of the same tree for different seasons. In this paper, we provide a comprehensive system to generate a new image of a different season from a given image by reflecting the seasonal characteristics. We construct a new color transfer approach through the HSV color space. We also create the generated season change more properly by identifying the foreground and background of the image. By knowing the foreground of a scene, the system ensures the color modification only apply to the main object which is detected. Once we make in-depth observations of a tree, we extract a tree characteristic from the particular flowers on these trees with different magnitudes and directions. Currently, we create a system that can deploy the flowers along particular paths by generating random scales and rotations. Edge detection and K-Means Clustering are used to find a reasonable place to grow the flowers. Additionally, this system can also be assisted with user interaction to mark the reasonable result. The collaboration between the system and the user would be essential to hint the foreground and background of the image and also to control the final result for an acceptable output.

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