MCA-NN: Multiple Correspondence Analysis Based Neural Network for Disaster Information Detection

This paper proposes a semantic content analysis framework for reliable video event detection. In this work, we target to improve the concept detection results by feeding the learnt results from individual shallow learning models into a generic model to dig out of the similarities in deeper layers. Compared to the deep learning models, the shallow learning models are memorizing rather than understanding the features. The proposed framework tackles the issue in shallow learning by integrating the strength of Multiple Correspondence Analysis (MCA) and Multilayer Perceptron (MLP) neural network. The low-level features are taken as the initial inputs for MCA-based models to abstract higher-level feature values. The output values further involve interaction in the neural network for better understanding. It earns the ability to put forward the arguments. The framework provides final decisions of video classifications by analyzing the decisions of every single frame from the network outputs.