Open Access

ARTICLE

A Fine-Grained Recognition Model based on Discriminative Region Localization and Efficient Second-Order Feature Encoding

Xiaorui Zhang^1,2,*, Yingying Wang², Wei Sun³, Shiyu Zhou², Haoming Zhang⁴, Pengpai Wang¹

1 College of Computer and Information Engineering, Nanjing Tech University, Nanjing, 211816, China
2 School of Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China
3 School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China
4 School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, 210044, China

* Corresponding Author: Xiaorui Zhang. Email:

(This article belongs to the Special Issue: Advances in Image Recognition: Innovations, Applications, and Future Directions)

Computers, Materials & Continua 2026, 87(1), 37 https://doi.org/10.32604/cmc.2025.072626

Received 31 August 2025; Accepted 18 November 2025; Issue published 10 February 2026

Abstract

Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition. However, existing data augmentation methods struggle to accurately locate discriminative regions in complex backgrounds, small target objects, and limited training data, leading to poor recognition. Fine-grained images exhibit “small inter-class differences,” and while second-order feature encoding enhances discrimination, it often requires dual Convolutional Neural Networks (CNN), increasing training time and complexity. This study proposes a model integrating discriminative region localization and efficient second-order feature encoding. By ranking feature map channels via a fully connected layer, it selects high-importance channels to generate an enhanced map, accurately locating discriminative regions. Cropping and erasing augmentations further refine recognition. To improve efficiency, a novel second-order feature encoding module generates an attention map from the fourth convolutional group of Residual Network 50 layers (ResNet-50) and multiplies it with features from the fifth group, producing second-order features while reducing dimensionality and training time. Experiments on Caltech-University of California, San Diego Birds-200-2011 (CUB-200-2011), Stanford Car, and Fine-Grained Visual Classification of Aircraft (FGVC Aircraft) datasets show state-of-the-art accuracy of 88.9%, 94.7%, and 93.3%, respectively.

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Keywords

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