Keypoint extraction has lately attracted attention in computer vision. Particularly, Scale-Invariant Feature Transform (SIFT) is one of them and invariant for scale, rotation and illumination change. In addition, the recent advance of machine learning becomes possible to recognize a lot of objects by learning large amount of keypoints. Recently, cloud system starts to be utilized to maintain large-scale database which includes learning keypoint. Some network devices have to access these systems and match keypoints. Kepoint extraction algorithm utilizes only spatial information. Thus, many unnecessary keypoints for recognition are detected. If only Keypoints of Interest (KOI) are extracted from input images, it achieves reduction of descriptor data and high-precision recognition. This paper proposes the keypoint selection algorithm from many keypoints including unnecessary ones based on spatio-temporal feature and Markov Random Field (MRF). This algorithm calculats weight on each keypoint using 3 kinds of features (intensity gradient, optical flow and previous state) and reduces noise by comparing with states of surrounding keypoints. The state of keypoints is connected by using the distance of keypoints. Evaluation results show that the 90% reduction of keypoints comparing conventional keypoint extraction by adding small computational complexity.