An Efficient Hierarchical Near-Duplicate Video Detection Algorithm Based on Deep Semantic Features

With the rapid development of the Internet and multimedia technology, the amount of multimedia data on the Internet is escalating exponentially, which has attracted much research attention in the field of Near-Duplicate Video Detection (NDVD). Motivated by the excellent performance of Convolutional Neural Networks (CNNs) in image classification, we bring the powerful discrimination ability of the CNN model to the NDVD system and propose a hierarchical detection method based on the derived deep semantic features from the CNN models. The original CNN features are firstly extracted from the video frames, and then a semantic descriptor and a labels descriptor are obtained respectively based on the basic content unit. Finally, a hierarchical matching scheme is proposed to promote fast near-duplicate video detection. The proposed approach has been tested on the widely used CC_WEB_VIDEO dataset, and has achieved state-of-the-art results with the mean Average Precision (mAP) of 0.977.