It is important for robots that act in human-centered environments to build image processing in a bottom-up manner. This paper proposes a method to autonomously acquire image feature extraction that is suitable for motion generation while moving in unknown environment. The proposed method extracts low level features without specifying image processing for robot body and obstacles. The position of body is acquired in image by clustering of SIFT features with motion information and state transition model is generated. Based on a learning model of adaptive addition of state transition model, collision relevant features are detected. Features that emerge when the robot can not move are acquired as collision relevant features. The proposed framework is evaluated with real images of the manipulator and an obstacle in obstacle avoidance.