Efficient multimedia information mining framework based on deep learning and self-organizing model

摘要

With the progress of society and the acceleration of urbanization, the problem of multimedia assisted urban traffic is becoming increasingly apparent. Intelligent transportation system arises at the historic moment, but the road traffic information data accumulation of intelligent transportation system is quite large, and the information analysis is complex. Usually, there are huge amounts of data stored in the database of the traffic system, thus we need to analyze and manage the data with scientific methods. As the visualized tools, the multimedia based analysis for the on-time data conditions should also be integrated, therefore, how to effectively model the scenario is challenging. In this paper, we introduce and analyze the structure and related model of recurrent neural network, and apply RNN to traffic big data mining model. According to the characteristics of traffic flow, this paper analyzes the causes of the error data in the process of traffic data collection, and puts forward the corresponding processing methods. The proposed model uses the TensorFlow for development, and applies the proposed deep learning model to implement traffic data mining, and we use charts to visually show the prediction results. Experimental results show that proposed algorithm can effectively mine large traffic multimedia data and has good robustness. At the same time, the prediction accuracy has reached 97.36%.