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Multimodal tasks based on attention mechanism and language face numerous problems. Based on multimodal hierarchical attention mechanism and genetic neural network, this paper studies the application of image segmentation algorithm in data completion and 3D scene reconstruction. The algorithm refers to the process of concentrating attention that humans subjectively pay attention to and calculates the difference between each pixel in the genetic neural network test image in the color space and the average value of the target image, which solves the problem of static feature maps and dynamic feature maps of image sequences. In addition, in view of the problem that the number of attention enhancement feature extraction modules is too large and the parameters are too large, the recursive mechanism is used as the feature extraction branch, and new model parameters are not added when the network depth is increased. The simulation results show that the accuracy of the improved image saliency detection algorithm based on the attention mechanism reaches 89.7%, and the difference between the average value of the single-point pixel and the target image is reduced to 0.132, which further promotes the practicability and reliability of the image segmentation model. [ABSTRACT FROM AUTHOR]

Multimodal biometrics fusion plays an important role in the field of biometrics. Therefore, this paper presents a multimodal biometrics fusion algorithm using deep reinforcement learning. In order to reduce the influence of user behavior, user's personal characteristics, and environmental light on image data quality, data preprocessing is realized through data transformation and single-mode biometric image region segmentation. A two-dimensional Gobar filter was used to analyze the texture of local sub-blocks, qualitatively describe the similarity between the filter and the sub-blocks and extract the phase information and local amplitude information of multimodal biometrics features. Deep reinforcement learning was used to construct the classifier of different modal biometrics, and the weighted sum fusion of different modal biometrics was implemented by fractional information. The multimodal biometrics fusion algorithm was designed. The Casia-iris-interval-v4 and NFBS datasets were used to test the performance of the proposed algorithm. The results show that the fused image quality is better, the feature extraction accuracy is between 84% and 93%, the average accuracy of feature classification is 97%, the multimodal biometric classification time is only 110 ms, the multimodal biometric fusion time is only 550 ms, the effect is good, and the practicability is strong. [ABSTRACT FROM AUTHOR]