Image key information processing using convolutional neural network and rotational invariant-hierarchical max pooling algorithm

In the information age, the effectiveness of image processing determines the quality of a large number of image analysis tasks. A fusion algorithm-based processing technique was proposed to process key image information. A feature dictionary was introduced as the matching template model and the standard model. The convolutional layer sampling feature block optimization was carried out using image segmentation ideas. The optimal threshold of the image to be segmented was obtained using the least squares method. The feature extraction layer was structurally supplemented and expressed at multiple scales in a two-dimensional linear graph. In the method training loss test, the research method achieved a loss value that dropped to near 0 after 32 iterations when training in low-contrast images. When testing the processing time of image key information, the research method achieved a processing time of 183ms when the image contained 6 features. When conducting scale ratio change testing, the research method achieved the highest image processing accuracy at a scale ratio of 1.0, which was 95.7%. This indicated that the research method had higher accuracy in processing key image information and higher efficiency. This research method can provide certain technical support for image recognition and feature extraction.