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International Journal of Frontiers in Engineering Technology, 2023, 5(2); doi: 10.25236/IJFET.2023.050206.

A study on fine-grained image classification algorithm based on ECA-NET and multi-granularity

Author(s)

Yabo Shang, Hua Huo

Corresponding Author:
Hua Huo
Affiliation(s)

College of Information Engineering, Henan University of Science and Technology, Kaiyuan Avenue 263, Luoyang, 471023, China

Abstract

The feature of large intra-class variance in fine-grained image classification is a challenge to the classification task. How to effectively learn the discriminant objects in the graph and find out the small discriminant regions is the key to classification. This paper proposes a weak-supervised fine-grained image classification algorithm based on multi-granularity feature fusion. The ECA module is fused with the classic network ResNet-50 to optimize the residual block to obtain a new basic network to enhance channel attention. Secondly, the local chaos module is introduced into the network to form a new image through random chaos regrouping so that the network can learn local regions with different scales of discrimination and obtain fine-grained feature expressions. The cooperative training of dual network branches makes the overall information and local information complement each other and have better expression. Experimental results on three widely used fine-grained image classification datasets verify that the proposed algorithm improves the accuracy of classification tasks and can effectively identify semantic sensitive features in images.

Keywords

fine-grained image classification; dual branch network; linear feature fusion; multi-granularity

Cite This Paper

Yabo Shang, Hua Huo. A study on fine-grained image classification algorithm based on ECA-NET and multi-granularity. International Journal of Frontiers in Engineering Technology (2023), Vol. 5, Issue 2: 31-38. https://doi.org/10.25236/IJFET.2023.050206.

References

[1] Zhang C, Chao L, Liang L, et al. Fine-Grained Image Classification via Low-Rank Sparse Coding With General and Class-Specific Codebooks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(7):1-10.

[2] Wu L, Wang Y, Li X, et al. Deep Attention-Based Spatially Recursive Networks for Fine-Grained Visual Recognition [J]. IEEE Transactions on Cybernetics, 2018:1791-1802.

[3] Dubey A, Gupta O, Raskar R, et al. Maximum-Entropy Fine-Grained Classification[C]// 2018.

[4] Krizhevsky A, Sutskever I, Hinton G. ImageNet Classification with Deep Convolutional Neural Networks [J]. Advances in neural information processing systems, 2012, 25(2).

[5] Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition [J]. Computer Science, 2014.

[6] Szegedy C, Wei L, Jia Y, et al. Going deeper with convolutions[C]// 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015.

[7] He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016.

[8] Howard A G, Zhu M, Chen B, et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications [J]. 2017.

[9] Wang Y, Wang Z. A survey of recent work on fine-grained image classification techniques [J]. Journal of Visual Communication and Image Representation, 2019.

[10] Wang D, Shen Z, Shao J, et al. Multiple Granularity Descriptors for Fine-Grained Categorization. 2015.

[11] Di L, Shen X, Lu C, et al. Deep LAC: Deep localization, alignment and classification for fine-grained recognition[C]// IEEE Conference on Computer Vision & Pattern Recognition. IEEE, 2015.

[12] Ge W, Lin X, Yu Y. Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up[C]// IEEE Conference on Computer Vision & Pattern Recognition. arXiv, 2019.

[13] He X, Peng Y, Zhao J. Which and How Many Regions to Gaze: Focus Discriminative Regions for Fine-Grained Visual Categorization [J]. International Journal of Computer Vision, 2019.

[14] Wang Y, Morariu V I, Davis L S. Learning a Discriminative Filter Bank within a CNN for Fine-grained Recognition [J]. 2016.

[15] Zheng H, Fu J, Zha Z J, et al. Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019.

[16] Du R, Chang D, Bhunia A K, et al. Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches[C]// 2020.

[17] Chen Y, Bai Y, Zhang W, et al. Destruction and construction learning for fine-grained image recognition[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019: 5157-5166.

[18] Wah C, Branson S, Welinder P, et al. The Caltech-UCSD Birds-200-2011 Dataset [J]. california institute of technology, 2011.

[19] Maji S, Rahtu E, Kannala J, et al. Fine-Grained Visual Classification of Aircraft [J]. HAL - INRIA, 2013.

[20] Krause J, Stark M, Deng J, et al. 3D Object Representations for Fine-Grained Categorization [C]// IEEE International Conference on Computer Vision Workshops. IEEE, 2014.

[21] Guo C, Lin Y, Xu M, et al. Inverse transformation sampling-based attentive cutout for fine-grained visual recognition[J]. The Visual Computer, 2022:1-12.

[22] Song K, Wei X S, Shu X, et al. Bi-Modal Progressive Mask Attention for Fine-Grained Recognition[J]. IEEE Transactions on Image Processing, 2020, PP (99):1-1.

[23] Zhou M, Bai Y, Zhang W, et al. Look-Into-Object: Self-Supervised Structure Modeling for Object Recognition[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020.

[24] Huang S, Wang X, Tao D. SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data[C]// National Conference on Artificial Intelligence. 2021.

[25] Luo W, Zhang H, Li J, et al. Learning Semantically Enhanced Feature for Fine-Grained Image Classification [J]. IEEE Signal Processing Letters, 2020, PP (99):1-1.

[26] Ding Y, Ma Z, Wen S, et al. AP-CNN: Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification [J]. IEEE Transactions on Image Processing, 2021, PP (99)

[27] Rong S, Wang Z, Wang J. Separated Smooth Sampling for Fine-grained Image Classification [J]. Neurocomputing, 2021, 461(5).

[28] Tk A, Kh A, Hb A. The Feature Generator of Hard Negative Samples for Fine-Grained Image Recognition [J]. Neurocomputing, 2020.

[29] Zhang Y, Sun Y, Wang N, et al. MSEC: Multi-Scale Erasure and Confusion for fine-grained image classification [J]. Neurocomputing, 2021, 449: 1-14.

[30] Chang D, Ding Y, Xie J, et al. The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification [J]. IEEE Transactions on Image Processing, 2020, PP (99):1-1.

[31] Ji J, Jiang L, Zhang T, et al. Adversarial erasing attention for fine-grained image classification[J]. Multimedia tools and applications, 2021(80-15).

[32] Lei J, Yang X, Yang S. Multiscale Progressive Complementary Fusion Network for Fine-Grained Visual Classification [J]. IEEE Access, 2022, 10: 62800-62810.

[33] Song J, Yang R. Feature Boosting, Suppression, and Diversification for Fine-Grained Visual Classification[C]// 2021.

[34] Du R, Chang D, Bhunia A K, et al. Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches[J]. 2020.