Automatic Image Processing Algorithm for Light Environment Optimization Based on Multimodal Neural Network Model

Abstract

In this paper, we conduct an in-depth study and analysis of the automatic image processing algorithm based on a multimodal Recurrent Neural Network (m-RNN) for light environment optimization. By analyzing the structure of m-RNN and combining the current research frontiers of image processing and natural language processing, we find out the problem of the ineffectiveness of m-RNN for some image generation descriptions, starting from both the image feature extraction part and text sequence data processing. Unlike traditional image automatic processing algorithms, this algorithm does not need to add complex rules manually. Still, it evaluates and filters through the training image collection and finally generates image automatic processing models by m-RNN. An image semantic segmentation algorithm is proposed based on multimodal attention and adaptive feature fusion. The main idea of the algorithm is to combine adaptive and feature fusion and then introduce data enhancement for small-scale multimodal light environment datasets by extracting the importance between images through multimodal attention. The model proposed in this paper can span the semantic differences of different modalities and construct feature relationships between different modalities to achieve an inferable, interpretable, and scalable feature representation of multimodal data. The automatic processing of light environment images using multimodal neural networks based on traditional algorithms eliminates manual processing and greatly reduces the time and effort of image processing.

Electron ionization mass spectrometry feature peak relationships combined with deep classification model to assist similarity algorithm for fast and accurate identification of compounds

“…Convolutional neural networks (CNN) are a class of feedforward neural networks that contain convolutional computation and have a deep structure, are one of the representative algorithms of deep learning (deep learning), and are especially suitable for processing image data 15,16 . In recent years, CNNs have been able to efficiently process these complex MS data by automatically learning the features in mass spectra.…”

“…where N Q is the number of peaks in the query spectrum and N BO is the number of peaks in the query and reference spectra, respectively; 15,16 In recent years, CNNs have been able to efficiently process these complex MS data by automatically learning the features in mass spectra. During the training process, CNNs can learn how to recognize and distinguish target compounds from other irrelevant compounds, and this ability makes CNNs a powerful tool for MS data analysis.…”