Near-Duplicate Video Retrieval with Deep Metric Learning

• Published in: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
• Main Authors: Kordopatis-Zilos, Giorgos, Papadopoulos, Symeon, Patras, Ioannis, Kompatsiaris, Yiannis
• Format: Conference Object
• Language: unknown
• Published: 2017
• Subjects: Near-Duplicate; Video Retrieval; Deep Metric Learning; Triplets; DML
• Online Access: https://zenodo.org/record/1051270; https://doi.org/10.1109/ICCVW.2017.49

This work addresses the problem of Near-Duplicate Video Retrieval (NDVR). We propose an effective video-level NDVR scheme based on deep metric learning that leverages Convolutional Neural Network (CNN) features from intermediate layers to generate discriminative global video representations in tandem with a Deep Metric Learning (DML) framework with two fusion variations, trained to approximate an embedding function for accurate distance calculation between two near-duplicate videos. In contrast to most state-of-the-art methods, which exploit information deriving from the same source of data for both development and evaluation (which usually results to dataset-specific solutions), the proposed model is fed during training with sampled triplets generated from an independent dataset and is thoroughly tested on the widely used CC_WEB_VIDEO dataset, using two popular deep CNN architectures (AlexNet, GoogleNet). We demonstrate that the proposed approach achieves outstanding performance against the state-of-the-art, either with or without access to the evaluation dataset.