Due to development of 3D display technology, industries related 3D have been grown. For this reason, the demand of 3D contents increases, but there is a short sup- ply of 3D contents. Consequently, research on 2D-to-3D conversion is underway. In 2D-to-3D conversion, the depth information of scene is obtained through an analysis of several depth cues on input sequence and the depth map corresponding to a scene can be generated by combining several depth cues and assigning an appropriate depth level. Scene classification for depth assignment is needed in this process. This paper classifies a scene into landscape, linear perspective, and normal type automatically. The proposed method analyzes landscape type and found there is a relation between image pattern and distribution of color and edge, and suggest the criteria for clas- sification. Moreover, the other criteria for linear perspective classification based on vanishing point detection is proposed. To verify performance, the proposed features are fed into a linear SVM classifier, and 651 images are used. Experiment results show that the algorithm has an advantages in performance by about 13%.
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