Real-time retrieval of similar videos with application to computer-aided retinal surgery

This paper introduces ongoing research on computer-aided ophthalmic surgery. In particular, a novel Content-Based Video Retrieval (CBVR) system is presented. Its purpose is the following: given a video stream captured by a digital camera monitoring the surgery, the system should retrieve, in real-time, similar video subsequences in video archives. In order to retrieve semantically-relevant videos, most existing CBVR systems rely on temporally flexible distance measures such as Dynamic Time Warping. These distance measures are slow and therefore do not allow real-time retrieval. In the proposed system, temporal flexibility is introduced in the way video subsequences are characterized, which allows the use of simple and fast distance measures. As a consequence, realtime retrieval of similar video subsequences, among hundreds of thousands of examples, is now possible. Besides, the proposed system is adaptive: a fast training procedure is presented. The system has been successfully applied to automated recognition of retinal surgery steps on a 69-video dataset: areas under the Receiver Operating Characteristic curves range from A_z=0.809 to A_z=0.989.