Hybrid Probabilistic Algorithm Design for High-Accuracy Image Recognition
Lingguo Zou, Meihua Zhang · 2025
This study discusses the performance bottlenecks of traditional image recognition algorithms in terms of feature extraction, network structure design, and computing resource allocation. A breakthrough adaptive convolutional network integrating statistical priors is proposed. At the same time, a hybrid probability model optimizes traditional image recognition algorithms. The core of the algorithm is to construct a set of statistical measurement functions based on the local complexity of the image. This measurement function includes multiple statistical feature indicators such as local variance, entropy value and gradient amplitude. These indicators are used to dynamically adjust the number and scale of convolution kernels. At the same time, the study designed a nonlinear mapping function with a multi-layer perceptron as the core. This operation realizes the coupling between convolution parameters and image complexity. In order to avoid information loss, an adaptive pooling mechanism is also introduced to maintain spatial structure information. On this basis, this study further constructed a hybrid probability classification module. This module uses Bayesian reasoning combined with neural network output to achieve the correction and optimization of category posterior probability. The comparative experimental results fully verify the effectiveness of the proposed algorithm in multiple typical image recognition tasks. In particular, the proposed algorithm shows strong robustness in scenarios with small samples, high noise and imbalanced category distribution.