Retrieval and Analysis of Multimedia Data of Robot Deep Neural Network Based on Deep Learning and Information Fusion

Abstract

In view of many problems of slow data information retrieval speed and low retrieval accuracy in the use of traditional data information retrieval systems, this paper proposes a robotic deep neural network multimedia data retrieval method based on deep learning and information fusion. By using deep learning combined with information fusion algorithms, we obtain a combination of lower-level features to form more abstract salient features in order to analyze the feature distribution characteristics of data information. This method can effectively solve the problem of "semantic gap" in the process of retrieval and analysis of multimedia data from robotic deep neural networks. At the same time, the robot deep neural network can realize optimization of the system hardware from multimedia data tracking, data mining and retrieval system warning to design the corresponding software design process. Finally, the results of the analysis by example show that: teaching multimedia information retrieval as an example for analysis, the multimedia information retrieval system proposed in this paper has fast retrieval speed and high accuracy, which can provide a perfect platform for the field of education and will become an important part of media data retrieval in the future.