Author(s)

Xin Yuan ¹, Chunhui Liang ¹, Jianye An ¹

Affiliation(s)

¹ School of science, Tianjin University of Commerce, Tianjin, 300134, China

Corresponding Author

Chunhui Liang

ABSTRACT

In high-dimensional image classification tasks, conventional convolutional neural networks (CNNs) suffer from high-frequency feature loss in pooling layers and limited robustness of Softmax classifiers when handling complex data distributions. In this paper, we propose a novel classification framework that combines multi-scale Haar wavelet pooling with a Gaussian Mixture Model Classifier (GMMC), which can simultaneously retain high-frequency information and optimize multimodal feature distributions. During model training, we integrate Selective Multiscale Wavelet Pooling (SMWP) into the Expectation-Maximization (EM) algorithm to enhance frequency-domain features and jointly improve classification accuracy. Our approach achieved classification accuracies of 97.17% on CIFAR-10 and 99.98% on SVHN, outperforming the MaxPooling + Softmax baseline by 7.32% and 4.93%, respectively. This research proposes a promising framework for fine-grained medical image classification, with potential applicability in low-light image enhancement and cross-modal retrieval tasks.

KEYWORDS

Image Classification, Haar Wavelet Pooling, Gaussian Mixture Model, Frequency-Domain Feature Preservation, Intra-Class Multimodality

CITE THIS PAPER

Xin Yuan, Chunhui Liang, Jianye An, Research on Image Classification Method Based on Haar Wavelet Pooling and Probabilistic Mixture Model. Journal of Image Processing Theory and Applications (2025) Vol. 8: 132-143. DOI: http://dx.doi.org/10.23977/jipta.2025.080116.

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