In this paper, we describe a supervised four-dimensional (4D) light field segmentation method that uses a graph-cut algorithm. Since 4D light field data has implicit depth information and contains redundancy, it differs from simple 4D hyper-volume. In order to preserve redundancy, we define two neighboring ray types (spatial and angular) in light field data. To obtain higher segmentation accuracy, we also design a learning-based likelihood, called objectness, which utilizes appearance and disparity cues. We show the effectiveness of our method via numerical evaluation and some light field editing applications using both synthetic and real-world light fields.