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

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Traditional image processing algorithms often require manual rule-setting, which is time-consuming and complex.
• Multimodal Recurrent Neural Networks (m-RNNs) show promise but face challenges in effectively processing image generation descriptions.
• Optimizing light environments through automated image analysis is crucial for various applications.

Purpose of the Study:

• To analyze and improve the effectiveness of m-RNNs for automatic image processing in light environments.
• To develop a novel image semantic segmentation algorithm leveraging multimodal attention and adaptive feature fusion.
• To address the limitations of existing m-RNNs in handling image feature extraction and text sequence data.

Main Methods:

• An in-depth analysis of m-RNN structure was performed, integrating current trends in image and natural language processing.
• A new image semantic segmentation algorithm was proposed, incorporating multimodal attention and adaptive feature fusion.
• Data enhancement techniques were applied to small-scale multimodal light environment datasets using multimodal attention.

Main Results:

• The proposed algorithm effectively spans semantic differences across modalities, constructing robust feature relationships.
• The model achieves inferable, interpretable, and scalable feature representation for multimodal data.
• Automated processing using multimodal neural networks significantly reduces manual effort and processing time.

Conclusions:

• The developed multimodal neural network approach offers an effective, automated solution for light environment image processing.
• The integration of multimodal attention and adaptive feature fusion enhances semantic segmentation accuracy.
• This research provides a scalable and interpretable method for multimodal data analysis in image processing tasks.