The F1 score for video search systems is computed by combining precision and recall into a single metric, balancing their trade-offs. Precision measures the fraction of retrieved videos that are relevant (true positives / (true positives + false positives)), while recall measures the fraction of relevant videos successfully retrieved (true positives / (true positives + false negatives)). The F1 score is the harmonic mean of these two values: F1 = 2 * (precision * recall) / (precision + recall). This penalizes extreme imbalances—for example, a system with high precision but low recall (or vice versa) will have a lower F1 score than one with moderate values for both.
In practice, video search systems often evaluate F1 at a specific cutoff (e.g., top 10 results). For example, if a query has 5 relevant videos in a dataset and the system returns 10 results with 3 correct matches, precision is 3/10 (30%) and recall is 3/5 (60%). The F1 score would then be (2 * 0.3 * 0.6) / (0.3 + 0.6) = 0.4 (40%). This approach assumes binary relevance judgments (a video is either relevant or not) and requires ground-truth annotations. For ranked results, some implementations use weighted averages across multiple queries or adjust for varying numbers of relevant items per query to avoid skewing the metric.
Key considerations include whether to compute F1 globally (across all queries) or per-query, and how to handle partial matches (e.g., temporal segments within videos). For temporal tasks, like detecting specific events in a video, F1 might incorporate overlap thresholds (e.g., Intersection over Union) to determine true positives. Developers should also decide whether to prioritize strict exact matches (common in retrieval) or allow for approximate relevance, which impacts how false positives/negatives are counted. Tools like scikit-learn’s classification_report or custom scripts are typically used to automate these calculations during evaluation.