Near-Duplicate Video Retrieval With Deep Metric Learning

Giorgos Kordopatis-Zilos, Symeon Papadopoulos, Ioannis Patras, Yiannis Kompatsiaris; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 347-356


This work addresses the problem of Near-Duplicate Video Retrieval (NDVR). We propose an efficient video-level NDVR scheme based on deep metric learning that leverages 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. We demonstrate that the proposed approach achieves outstanding performance against the state-of-the-art, either with or without access to the evaluation dataset.

Related Material

author = {Kordopatis-Zilos, Giorgos and Papadopoulos, Symeon and Patras, Ioannis and Kompatsiaris, Yiannis},
title = {Near-Duplicate Video Retrieval With Deep Metric Learning},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}