Hashtag Recommendation for Micro Videos Using 3D Convolutional Neural Network

Abstract

In recent years, due to common usage of camera equipment like mobile phones and variations of various short videos platform, a lot of videos published each second are either creative or non-creative. Compared to short videos creation, traditional video creation process is very long process like time consuming, producing & casting. Creating shot video is easy where you can use any smart device’s camera, a video is creative if it creates a meaningful interest in your mind after watching else non creative. In this paper we focused on a deep learning algorithm for understanding consistent features and complementary features of micro videos in vine dataset using 3-dimensional convolutional network. The algorithm works on equal-sized frames of video to extract & learn features such as spatial features where we train the model on three different modules of vine dataset d60, d80 & d 100 of vine. We also perform batch normalization on convolutional outputs to avoid overfit & got best results for given vine test data. Through experimental practice we found that 3D CNN performs better than previous methods of understanding video method. In addition to given algorithm we found that how different training dataset affect the feature extraction and affect the results