Research on Intelligent System of Multimodal Deep Learning in Image Recognition
Ting Xu, Iris Li, Qishi Zhan, Yuxiang Hu, Haowei Yang
Abstract
In this paper, a multi-scale image estimation method based on wavelet transform is proposed, which can effectively remove motion features from multiple videos. Then the autoencoder with sparsity limit is used to adjust the input signal to achieve effective compression. The effective features are extracted and the optimal unique vector is learned. The improved convolutional neural network is used to recognize weak moving objects. Experiments show that the algorithm can achieve high accuracy without large-scale learning samples, and the highest recognition rate is 99.36%. This algorithm has a great improvement over conventional algorithm.
Topics & Concepts
AutoencoderArtificial intelligenceComputer sciencePattern recognition (psychology)Convolutional neural networkWavelet transformImage (mathematics)Deep learningWaveletScale (ratio)Computer visionPhysicsQuantum mechanicsHuman Pose and Action RecognitionAdvanced Technologies in Various FieldsVideo Surveillance and Tracking Methods