PURPOSE: Over the last decade, the demand for content management of video recordings of surgical procedures has greatly increased. Although a few research methods have been published toward this direction, the related literature is still in its infancy. In this paper, we address the problem of shot detection in endoscopic surgery videos, a fundamental step in content-based video analysis.METHODS: The video is first decomposed into short clips that are processed sequentially. After feature extraction, we employ spatiotemporal Gaussian mixture models (GMM) for each clip and apply a variational Bayesian (VB) algorithm to approximate the posterior distribution of the model parameters. The proper number of components is handled automatically by the VBGMM algorithm. The estimated components are matched along the video sequence via their Kullback-Leibler divergence. Shot borders are defined when component tracking fails, signifying a different visual appearance of the surgical scene.RESULTS: Experimental evaluation was performed on laparoscopic videos containing a variable number of shots. Performance was measured via precision, recall, coverage and overflow metrics. The proposed method was compared with GMM and a shot detection method based on spatiotemporal motion differences (MotionDiff). The results demonstrate that VBGMM has higher performance than all other methods for most assessment metrics: precision and recall >80 %, coverage: 84 %. Overflow for VBGMM was worse than MotionDiff (37 vs. 27 %).CONCLUSIONS: The proposed method generated promising results for shot border detection. Spatiotemporal modeling via VBGMMs provides a means to explore additional applications such as component tracking.