Feature matching is fundamental to many vision tasks. Due to the low visibility of images in underwater environments, traditional pixels-based matching methods suffer from miss-matching or error-matching. Recently, Superpixel based features have been applied to image feature analysis. However, most of existing methods dedicate to rectified stereo matching with images captured in the air. This paper presents a novel feature matching scheme aiming at underwater images. It targets the unrectified

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... mage pair from the video sequence. The Superpixel matching process is fulfilled with multiclass labelling based on Markov Random Field (MRF). Experiments show that the proposed method produces competitive performance.