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This paper proposes a framework for content-based video retrieval that addresses various tasks as particular event retrieval, copy detection or video synchronization. Given a video query, the method is able to efficiently retrieve, from a large collection, similar video events or near-duplicates with temporarily consistent excerpts. As a byproduct of the representation, it provides a precise temporal alignment of the query and the detected video excerpts. Our method converts a series of frame descriptors into a single visual-temporal descriptor, called a temporal invariant match kernel. This representation takes into account the relative positions of the visual frames: the frame descriptors are jointly encoded with their timestamps. When matching two videos, the method produces a score function for all possible relative timestamps, which is maximized to obtain both the similarity score and the relative time offset. Then, we propose two complementary contributions to further improve the detection and localization performance. The first is a novel query expansion method that takes advantage of the joint descriptor/timestamp representation to automatically align the first result set and produce an enriched temporal query. In contrast to other query expansion methods proposed for videos, it preserves the localization capability. Second, we improve the localization trade-off between quality and representation size by using several complementary temporal match kernels. We evaluate our approach on benchmarks for particular event retrieval, copy detection and video synchronization. Our experiments show that our approach achieve excellent detection and localization results.

This work summarizes the findings of the 7th iteration of the Video Browser Showdown (VBS) competition organized as a workshop at the 24th International Conference on Multimedia Modeling in Bangkok. The competition focuses on video retrieval scenarios in which the searched scenes were either previously observed or described by another person (i.e., an example shot is not available). During the event, nine teams competed with their video retrieval tools in providing access to a shared video collection with 600 hours of video content. Evaluation objectives, rules, scoring, tasks, and all participating tools are described in the article. In addition, we provide some insights into how the different teams interacted with their video browsers, which was made possible by a novel interaction logging mechanism introduced for this iteration of the VBS. The results collected at the VBS evaluation server confirm that searching for one particular scene in the collection when given a limited time is still a challenging task for many of the approaches that were showcased during the event. Given only a short textual description, finding the correct scene is even harder. In ad hoc search with multiple relevant scenes, the tools were mostly able to find at least one scene, whereas recall was the issue for many teams. The logs also reveal that even though recent exciting advances in machine learning narrow the classical semantic gap problem, user-centric interfaces are still required to mediate access to specific content. Finally, open challenges and lessons learned are presented for future VBS events.

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