scholarly journals Bilinear CNN Model for Fine-Grained Classification Based on Subcategory-Similarity Measurement

2019 ◽  
Vol 9 (2) ◽  
pp. 301 ◽  
Author(s):  
Xinghua Dai ◽  
Shengrong Gong ◽  
Shan Zhong ◽  
Zongming Bao
Keyword(s):  
Background Noise ◽  
Supervised Classification ◽  
State Of The Art ◽  
Equal Treatment ◽  
Training Set ◽  
Significant Similarity ◽  
Fine Grained ◽  
Triplet Loss ◽  
Weakly Supervised ◽  
Weakly Supervised Classification

One of the challenges in fine-grained classification is that subcategories with significant similarity are hard to be distinguished due to the equal treatment of all subcategories in existing algorithms. In order to solve this problem, a fine-grained image classification method by combining a bilinear convolutional neural network (B-CNN) and the measurement of subcategory similarities is proposed. Firstly, an improved weakly supervised localization method is designed to obtain the bounding box of the main object, which allows the model to eliminate the influence of background noise and obtain more accurate features. Then, sample features in the training set are computed by B-CNN so that the fuzzing similarity matrix for measuring interclass similarities can be obtained. To further improve classification accuracy, the loss function is designed by weighting triplet loss and softmax loss. Extensive experiments implemented on two benchmarks datasets, Stanford Cars-196 and Caltech-UCSD Birds-200-2011 (CUB-200-2011), show that the newly proposed method outperforms in accuracy several state-of-the-art weakly supervised classification models.

scholarly journals Centralized Ranking Loss with Weakly Supervised Localization for Fine-Grained Object Retrieval

2018 ◽  
Author(s):  
Xiawu Zheng ◽  
Rongrong Ji ◽  
Xiaoshuai Sun ◽  
Yongjian Wu ◽  
Feiyue Huang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Feature Learning ◽  
Target Object ◽  
Object Retrieval ◽  
Unified Framework ◽  
Fine Grained ◽  
Discriminative Feature ◽  
Triplet Loss ◽  
Weakly Supervised ◽  
Ranking Loss

Fine-grained object retrieval has attracted extensive research focus recently. Its state-of-the-art schemesare typically based upon convolutional neural network (CNN) features. Despite the extensive progress, two issues remain open. On one hand, the deep features are coarsely extracted at image level rather than precisely at object level, which are interrupted by background clutters. On the other hand, training CNN features with a standard triplet loss is time consuming and incapable to learn discriminative features. In this paper, we present a novel fine-grained object retrieval scheme that conquers these issues in a unified framework. Firstly, we introduce a novel centralized ranking loss (CRL), which achieves a very efficient (1,000times training speedup comparing to the triplet loss) and discriminative feature learning by a ?centralized? global pooling. Secondly, a weakly supervised attractive feature extraction is proposed, which segments object contours with top-down saliency. Consequently, the contours are integrated into the CNN response map to precisely extract features ?within? the target object. Interestingly, we have discovered that the combination of CRL and weakly supervised learning can reinforce each other. We evaluate the performance ofthe proposed scheme on widely-used benchmarks including CUB200-2011 and CARS196. We havereported significant gains over the state-of-the-art schemes, e.g., 5.4% over SCDA [Wei et al., 2017]on CARS196, and 3.7% on CUB200-2011.  

scholarly journals Weakly Supervised Classification of Hyperspectral Image Based on Complementary Learning

2021 ◽  
Vol 13 (24) ◽  
pp. 5009
Author(s):  
Lingbo Huang ◽  
Yushi Chen ◽  
Xin He
Keyword(s):  
Supervised Classification ◽  
Data Augmentation ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
The State ◽  
Weak Supervision ◽  
Training Samples ◽  
Weakly Supervised ◽  
Weakly Supervised Classification

In recent years, supervised learning-based methods have achieved excellent performance for hyperspectral image (HSI) classification. However, the collection of training samples with labels is not only costly but also time-consuming. This fact usually causes the existence of weak supervision, including incorrect supervision where mislabeled samples exist and incomplete supervision where unlabeled samples exist. Focusing on the inaccurate supervision and incomplete supervision, the weakly supervised classification of HSI is investigated in this paper. For inaccurate supervision, complementary learning (CL) is firstly introduced for HSI classification. Then, a new method, which is based on selective CL and convolutional neural network (SeCL-CNN), is proposed for classification with noisy labels. For incomplete supervision, a data augmentation-based method, which combines mixup and Pseudo-Label (Mix-PL) is proposed. And then, a classification method, which combines Mix-PL and CL (Mix-PL-CL), is designed aiming at better semi-supervised classification capacity of HSI. The proposed weakly supervised methods are evaluated on three widely-used hyperspectral datasets (i.e., Indian Pines, Houston, and Salinas datasets). The obtained results reveal that the proposed methods provide competitive results compared to the state-of-the-art methods. For inaccurate supervision, the proposed SeCL-CNN has outperformed the state-of-the-art method (i.e., SSDP-CNN) by 0.92%, 1.84%, and 1.75% in terms of OA on the three datasets, when the noise ratio is 30%. And for incomplete supervision, the proposed Mix-PL-CL has outperformed the state-of-the-art method (i.e., AROC-DP) by 1.03%, 0.70%, and 0.82% in terms of OA on the three datasets, with 25 training samples per class.

Multiple kernel-based dictionary learning for weakly supervised classification

2015 ◽  
Vol 48 (8) ◽  
pp. 2667-2675 ◽  
Author(s):  
Ashish Shrivastava ◽  
Jaishanker K. Pillai ◽  
Vishal M. Patel
Keyword(s):  
Dictionary Learning ◽  
Supervised Classification ◽  
Multiple Kernel ◽  
Weakly Supervised ◽  
Weakly Supervised Classification

scholarly journals Weakly Supervised Classification For High Energy Physics

2018 ◽  
Vol 1085 ◽  
pp. 042006 ◽  
Author(s):  
Lucio Mwinmaarong Dery ◽  
Benjamin Nachman ◽  
Francesco Rubbo ◽  
Ariel Schwartzman
Keyword(s):  
Supervised Classification ◽  
High Energy Physics ◽  
High Energy ◽  
Weakly Supervised ◽  
Weakly Supervised Classification ◽  
Energy Physics

scholarly journals Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Jason A. Fries ◽  
Paroma Varma ◽  
Vincent S. Chen ◽  
Ke Xiao ◽  
Heliodoro Tejeda ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Cardiac Mri ◽  
Supervised Classification ◽  
Mri Sequences ◽  
Weakly Supervised ◽  
Weakly Supervised Classification
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