Temporal aggregation for large-scale query-by-image video retrieval

We address the challenge of using image queries to retrieve video clips from a large database. Using binarized Fisher Vectors as global signatures, we present three novel contributions. First, an asymmetric comparison scheme for binarized Fisher Vectors is shown to boost retrieval performance by 0.27 mean Average Precision, exploiting the fact that query images contain much less clutter than database videos. Second, aggregation of frame-based local features over shots is shown to achieve retrieval performance comparable to aggregation of those local features over single frames, while reducing retrieval latency and memory requirements by more than 3X. Several shot aggregation strategies are compared and results indicate that most perform equally well. Third, aggregation over scenes, in combination with shot signatures, is shown to achieve one order of magnitude faster retrieval at comparable performance. Scene aggregation also outperforms the recently proposed aggregation in random groups.