Enhancing image feature representation through maximum gaussianity discriminative normalization flow

Le résumé fourni par la source

Abstract Convolutional Neural Networks (CNNs) have achieved significant success in feature representation. However, their feature representation capability can be affected by high crossover points between classes within datasets, resulting in non-Gaussian and heterogeneous distributions. To address this, we propose a novel feature representation method called CNN-MGDNF, which captures features through convolution and pooling operations. A maximum Gaussianity normalizing flow (MGDNF) model is designed to transform the original feature space into a Gaussian space, balancing intervals between classes. Additionally, an entropy-based imbalance degree (EID) is explored to mitigate multi-class imbalance issues. Extensive experiments on the yak, vehicle identification, and bird species datasets demonstrate that CNN-MGDNF enhances feature representation, achieving remarkable accuracies of 99.59%, 97.93%, and 90.39%, respectively. Our method significantly outperforms traditional CNN models and other methods, underscoring its effectiveness in visual recognition tasks.

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