Deep Video Hashing Using 3DCNN with BERT
International journal of intelligent engineering and systems · 2022
Deep video hashing (DVH) is a very appealing way to decrease storage costs and query times.In this work we propose a hashing model using two separated modules.A 3DCNN is proposed with a bidirectional encoder representations from transformers (BERT) layer.And a hashing neural network (NN) module will learn to encode those features into hash codes.The proposed model that separates feature extraction from hash generation process results in better performance with respect to training time consumption and accuracy.We achieve a significant improvement in video retrieval performance on two benchmark datasets compared to state-of-the-art deep learning models for video retrieval that use convolutional neural networks (CNN)s or 3DCNNs along with other temporal feature extraction techniques and supervised hashing methods.For UCF101, HMDB51 datasets, more than 2 % mAP and 24 % improvement is achieved respectively for tested bit sizes.