Performance characterization of 2D CNN features for partial video copy detection
- Authors
- Publication Date
- Sep 26, 2023
- Identifiers
- DOI: 10.1007/978-3-031-44237-7_20
- OAI: oai:HAL:hal-04231596v1
- Source
- HAL-Descartes
- Keywords
- Language
- English
- License
- Unknown
- External links
Abstract
2D CNN are main components for Partial Video Copy Detection (PVCD). 2D CNN features serve for the retrieval and matching of videos. Robustness is a key property of these features. It is a well-known problem in the computer vision field but little investigated for PVCD. The contributions of this paper are twofold: (i) based on a public video dataset, we provide large-scale experiments with 700 B of comparisons of 4.4 M feature vectors. We report conclusions for PVCD consistent with the state-of-the-art. (ii) the regular protocol for performance characterization is misleading for PVCD as it is bounded to the video level. A method for the characterization of key-frames with 2D CNN features is proposed. It is based on a goodness criterion and a time series modelling. It provides a fine categorization of key-frames and allows a deeper characterization of a PVCD problem with 2D CNN features.