Research On Image Classification Algorithm Based On Gamma Mixture Model

With the rapid development of digital information technology,the number and categories of digital images have shown an explosive growth trend.How to effectively organize and manage these images is a challenging topic,and image classification is an effective way to solve this problem.In recent years,the image classification method based on the probabilistic mixture model has received widespread attention from scholars at home and abroad.Traditional mixture model clustering generally uses gaussian distribution assumptions to build image classification models,mainly due to the ease of implementation of its parameter estimation.However,image data have very different properties from Gaussian distributions,such as asymmetry,boundedness,etc.,and the characteristics of these image data obviously follow non-Gaussian distributions.Although non-Gaussian image features can be modeled and analyzed in traditional ways based on inverse transformation sampling and linear transformation,these methods cannot accurately describe the non-Gaussian nature of the image data,thus affecting the classification performance of the model.In addition,the probabilistic mixture model has a high computational cost of learning algorithms and is difficult to adapt to the requirements of large-scale image classification tasks.Firstly,aiming at the problem of speed processing in the traditional variational inference process of Gamma mixture model,a stochastic variational method based on gamma mixture model is proposed.A parameter estimation algorithm that can be described by a closed solution expression is derived by using the stochastic variational inference method.First of all,the data is randomly extracted,and the idea of random sampling is used for data extraction.Secondly,the local variational parameters are solved by randomly sampled,and then the intermediate global variational parameters are obtained.Finally the global variational parameters are obtained through the global variational parameters of the previous iteration and the intermediate global variational parameters of this iteration.Secondly,aiming at the processing efficiency of large-scale image classification tasks,an image classification method based on stochastic variational inference based on convolutional neural network and gamma mixture model is proposed.Firstly,the deep features of the image are extracted by convolutional neural network to accurately model the internal structure of the image features.Then the potential distribution of the image features is modeled by the gamma mixture model.Finally the extracted image features are classified based on the potential distribution of the constructed gamma mixture model.Finally,the actual image classification application of real data sets CIFAR-10 and Fashion-MNIST verifies the effectiveness and feasibility of the proposed algorithm.Experiments show that it has good calculation speed and classification accuracy,and provides an effective technical means for solving large-scale image classification problems.