Fine-Grained Image Classification Based on Target Acquisition and Feature Fusion

2021 ◽  
pp. 209-221
Author(s):  
Yan Chu ◽  
Zhengkui Wang ◽  
Lina Wang ◽  
Qingchao Zhao ◽  
Wen Shan
Keyword(s):  
Image Classification ◽  
Feature Fusion ◽  
Target Acquisition ◽  
Fine Grained

scholarly journals Weakly Supervised Fine-Grained Image Classification via Salient Region Localization and Different Layer Feature Fusion

2020 ◽  
Vol 10 (13) ◽  
pp. 4652
Author(s):  
Fangxiong Chen ◽  
Guoheng Huang ◽  
Jiaying Lan ◽  
Yanhui Wu ◽  
Chi-Man Pun ◽  
...  
Keyword(s):  
Image Classification ◽  
Feature Fusion ◽  
Classification Performance ◽  
Training Data ◽  
Classification Model ◽  
Global Features ◽  
Salient Region ◽  
Fine Grained ◽  
Proposed Model ◽  
Weakly Supervised

The fine-grained image classification task is about differentiating between different object classes. The difficulties of the task are large intra-class variance and small inter-class variance. For this reason, improving models’ accuracies on the task heavily relies on discriminative parts’ annotations and regional parts’ annotations. Such delicate annotations’ dependency causes the restriction on models’ practicability. To tackle this issue, a saliency module based on a weakly supervised fine-grained image classification model is proposed by this article. Through our salient region localization module, the proposed model can localize essential regional parts with the use of saliency maps, while only image class annotations are provided. Besides, the bilinear attention module can improve the performance on feature extraction by using higher- and lower-level layers of the network to fuse regional features with global features. With the application of the bilinear attention architecture, we propose the different layer feature fusion module to improve the expression ability of model features. We tested and verified our model on public datasets released specifically for fine-grained image classification. The results of our test show that our proposed model can achieve close to state-of-the-art classification performance on various datasets, while only the least training data are provided. Such a result indicates that the practicality of our model is incredibly improved since fine-grained image datasets are expensive.

Fine-Grained Image Classification Based on Multi-Scale Feature Fusion

2020 ◽  
Vol 57 (12) ◽  
pp. 121002
Author(s):  
李思瑶 Li Siyao ◽  
刘宇红 Liu Yuhong ◽  
张荣芬 Zhang Rongfen
Keyword(s):  
Image Classification ◽  
Feature Fusion ◽  
Scale Feature ◽  
Fine Grained ◽  
Multi Scale

scholarly journals Two-Level Attentions and Grouping Attention Convolutional Network for Fine-Grained Image Classification

2019 ◽  
Vol 9 (9) ◽  
pp. 1939 ◽  
Author(s):  
Yadong Yang ◽  
Xiaofeng Wang ◽  
Quan Zhao ◽  
Tingting Sui
Keyword(s):  
Image Classification ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Fine Tuning ◽  
Semantic Features ◽  
Convolutional Network ◽  
Attention Model ◽  
Fine Grained ◽  
Visual Attention Mechanism ◽  
High Level

The focus of fine-grained image classification tasks is to ignore interference information and grasp local features. This challenge is what the visual attention mechanism excels at. Firstly, we have constructed a two-level attention convolutional network, which characterizes the object-level attention and the pixel-level attention. Then, we combine the two kinds of attention through a second-order response transform algorithm. Furthermore, we propose a clustering-based grouping attention model, which implies the part-level attention. The grouping attention method is to stretch all the semantic features, in a deeper convolution layer of the network, into vectors. These vectors are clustered by a vector dot product, and each category represents a special semantic. The grouping attention algorithm implements the functions of group convolution and feature clustering, which can greatly reduce the network parameters and improve the recognition rate and interpretability of the network. Finally, the low-level visual features and high-level semantic information are merged by a multi-level feature fusion method to accurately classify fine-grained images. We have achieved good results without using pre-training networks and fine-tuning techniques.

Attention-Based Multi-Task Learning For Fine-Grained Image Classification

2021 ◽  
Author(s):  
Dichao Liu ◽  
Yu Wang ◽  
Kenji Mase ◽  
Jien Kato
Keyword(s):  
Image Classification ◽  
Fine Grained ◽  
Task Learning

scholarly journals Mask Guided Attention For Fine-Grained Patchy Image Classification

2021 ◽  
Author(s):  
Jun Wang ◽  
Xiaohan Yu ◽  
Yongsheng Gao
Keyword(s):  
Image Classification ◽  
Fine Grained

Object Decoupling with Graph Correlation for Fine-Grained Image Classification

2021 ◽  
Author(s):  
Qiushi Guo ◽  
Mingchen Zhuge ◽  
Dehong Gao ◽  
Huiling Zhou ◽  
Xin Wang ◽  
...  
Keyword(s):  
Image Classification ◽  
Fine Grained

scholarly journals Adaptive weights learning in CNN feature fusion for crime scene investigation image classification

2021 ◽  
pp. 1-16
Author(s):  
Liu Ying ◽  
Zhang Qian Nan ◽  
Wang Fu Ping ◽  
Chiew Tuan Kiang ◽  
Lim Keng Pang ◽  
...  
Keyword(s):  
Image Classification ◽  
Feature Fusion ◽  
Crime Scene ◽  
Crime Scene Investigation ◽  
Adaptive Weights ◽  
Scene Investigation

scholarly journals High-Resolution SAR Image Classification Using Multi-Scale Deep Feature Fusion and Covariance Pooling Manifold Network

2021 ◽  
Vol 13 (2) ◽  
pp. 328
Author(s):  
Wenkai Liang ◽  
Yan Wu ◽  
Ming Li ◽  
Yice Cao ◽  
Xin Hu
Keyword(s):  
High Resolution ◽  
Image Classification ◽  
Feature Fusion ◽  
Representation Learning ◽  
Sar Image ◽  
Gabor Filtering ◽  
Feature Maps ◽  
Sar Images ◽  
Multi Scale ◽  
Deep Feature

The classification of high-resolution (HR) synthetic aperture radar (SAR) images is of great importance for SAR scene interpretation and application. However, the presence of intricate spatial structural patterns and complex statistical nature makes SAR image classification a challenging task, especially in the case of limited labeled SAR data. This paper proposes a novel HR SAR image classification method, using a multi-scale deep feature fusion network and covariance pooling manifold network (MFFN-CPMN). MFFN-CPMN combines the advantages of local spatial features and global statistical properties and considers the multi-feature information fusion of SAR images in representation learning. First, we propose a Gabor-filtering-based multi-scale feature fusion network (MFFN) to capture the spatial pattern and get the discriminative features of SAR images. The MFFN belongs to a deep convolutional neural network (CNN). To make full use of a large amount of unlabeled data, the weights of each layer of MFFN are optimized by unsupervised denoising dual-sparse encoder. Moreover, the feature fusion strategy in MFFN can effectively exploit the complementary information between different levels and different scales. Second, we utilize a covariance pooling manifold network to extract further the global second-order statistics of SAR images over the fusional feature maps. Finally, the obtained covariance descriptor is more distinct for various land covers. Experimental results on four HR SAR images demonstrate the effectiveness of the proposed method and achieve promising results over other related algorithms.

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