Research On The Multi-semantic Image Processing Algorithms Based On Zero-Shot Learning

In the era of rapid development of big data and artificial intelligence,digital images,as one of the important carriers of human information and knowledge,play a huge role in our daily work and life.With the development of artificial neural networks and machine learning,many traditional image processing algorithms are gradually being replaced by machine learning-based methods.Although the traditional deep learning image processing algorithm has made great progress,because the traditional supervised learning algorithm requires a large number of image samples,it will lead to training failure in the scenario of missing data samples.To address the problem of missing samples in certain classes,this thesis focuses on the research of various image processing algorithms based on zero-shot learning,using multi-semantic information to achieve classification of unseen classes of images,further realizing pixel-level semantic segmentation of unseen classes,and proposing a new software design pattern for zero-shot image retrieval to meet the application requirements of image processing algorithms in scenarios with missing category samples.The research contents include zero-shot learning algorithms based on dual autoencoder,zero-shot multi-semantic segmentation network based on spatial feature embedding,and zero-shot image retrieval system design based on twin interface design pattern.The specific research content and contributions are as follows:(1)To address the bias problem in the generalization testing environment of existing zero-shot learning algorithms,a zero-shot learning algorithm that combines dual autoencoder and additional latent space classifier is proposed to alleviate the bias problem.To address the problem of model collapse during training in existing zero-shot learning algorithms based on feature generation,a novel embedding-based zero-shot learning mechanism based on independent latent space is proposed,and various effective spatial constraint loss functions such as visual-semantic consistency loss and dual loss are designed in the latent space to guide the model to learn suitable parameters.In experiments,the dual autoencoder loss,visual-semantic consistency loss,and additional latent space classification loss complement each other,resulting in a 15.2%improvement on accuracy in zero-shot learning compared to traditional models in the generalization scenario.(2)To address the problem that existing semantic segmentation networks are difficult to generalize to unseen classes,a new zero-shot multi-semantic segmentation network algorithm based on spatial embedding is proposed.To address the bias probelm in zeroshot semantic segmentation networks under the generalization zero-shot setting,various pixel-level constraints are designed in the latent space to achieve semantic segmentation of unseen class images.In experiments,the proposed algorithm achieves an 8.8%improvement on mIoU compared to existing zero-shot semantic segmentation models and has greater practical value in real-world scenarios.(3)To address the problem that traditional object-oriented software design patterns cannot effectively expand on behavior,a new twin interface design pattern is proposed to solve the expression problem.To address the problem that existing text-based image retrieval systems lack the ability to handle unseen classes,a novel image retrieval system based on twin interface and zero-shot learning is designed.The proposed zero-shot learning research results and new software design methods are applied to achieve a retrieval system for unseen class images.