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Multi-Metric Induced Robust Graph

Zengmin Geng, Xiaodong Sun, Jianxia Du, Jujian Zhang, Yiling Zhou
2021 IEEE Access  
The key issue of the graph-based applications is to construct an informative graph to effectively represent data correlations.  ...  Graph-based learning model has a wide range of applications in machine learning and computer vision.  ...  IMAGE CLASSIFICATION EXPERIMENTS The graph-based classification model adopts the combination of classification loss function and the graph regularizer for classification task.  ... 
doi:10.1109/access.2021.3057901 fatcat:vjcviidrxneuxhw6dk6xdhkufq

Visual Reranking with Local Learning Consistency [chapter]

Xinmei Tian, Linjun Yang, Xiuqing Wu, Xian-Sheng Hua
2010 Lecture Notes in Computer Science  
The graph-based reranking methods have been proven effective in image and video search.  ...  The basic assumption behind them is the ranking score consistency, i.e., neighboring nodes (visually similar images or video shots) in a graph having close ranking scores, which is modeled through a regularizer  ...  ., classification-based [8, 13] , clusteringbased [3] and graph-based [4, 6, 7, 10] .  ... 
doi:10.1007/978-3-642-11301-7_19 fatcat:6dsnfv64ezgi3inx4soia5fs3i

Ensemble learning via feature selection and multiple transformed subsets: Application to image classification

A. Khoder, F. Dornaika
2021 Applied Soft Computing  
These are associated with subsets of ranked original features. Multiple feature subsets were used for estimating the transformations.  ...  Instead of deploying multiple classifiers on top of the transformed features, we target the estimation of multiple extracted feature subsets obtained by multiple learned linear embeddings.  ...  Robust Discriminant Analysis using Gradient Descent RDA_GD [39] , Linear Regression Based Classification (LRC) [68] , Low-rank Linear Regression (LRLR) [69] , Low-rank Ridge Regression (LRRR) [69]  ... 
doi:10.1016/j.asoc.2021.108006 fatcat:43go5wmauzao5ggkt2jmxkmeja

Attribute Based Image Search Re-Ranking

2015 International Journal of Science and Research (IJSR)  
Based on the classifiers for all the predefined attributes, each image is represented by an attribute feature consisting of the responses from these classifiers.  ...  Its basic principle is that visually similar images should have similar ranking scores. It improves the performance over the text-based image search engine.  ...  Regularized logistic regression trained for each within each class.  ... 
doi:10.21275/v4i11.nov151336 fatcat:j6lepns43zf3xb3vnt6q245kba

Re-ranking with Click-Base Similarity and Typicality using Spectral Clustering

Mayuri Kawalkar, Gangotri Nathaney
2017 IARJSET  
Xiaopeng Yang, yangdongzhang, Ting Yao, Tao Mei proposed new re-ranking algorithm name click boosting multimodality graph base re-ranking.  ...  The algorithm leverages click image to put similar image that are not clicked. And reranked them in multimodality graph base scheme.  ...   Classification using SVM Classifier SVM classifier is a supervised machine learning algorithm which can be used for classification or regression problems.  ... 
doi:10.17148/iarjset.2017.4611 fatcat:cqvqbdkumzhptjfpqwbz2diwsi

Special issue on "visual semantic analysis with weak supervision"

Luming Zhang, Yang Yang, Rongrong Ji, Roger Zimmermann
2017 Multimedia Systems  
1 National University of Singapore, Singapore, Singapore Acknowledgments We also thank the reviewers for their efforts to guarantee the high quality of this special issue.  ...  In "Graph-based Clustering and Ranking for Diversified Image Search", Yan et al. described a novel framework for Web image search results clustering and re-ranking, the goal is to improve diversity at  ...  In "Semi-supervised Tensor Learning for Image Classification", Zhang et al. proposed a new tensor-based representation algorithm for image classification.  ... 
doi:10.1007/s00530-016-0527-4 fatcat:72hcjiiwfzbk7mdrsumuia7rzy

A Closed Form Solution to Multi-View Low-Rank Regression [article]

Shuai Zheng, Xiao Cai, Chris Ding, Feiping Nie, Heng Huang
2016 arXiv   pre-print
For example, in computer vision, we can describe an image using different image features, such as pixel intensity, color, HOG, GIST feature, SIFT features, etc..  ...  In this paper, we propose a multi-view low-rank regression model by imposing low-rank constraints on multi-view regression model.  ...  Overall, the Sum voting method is better for regression based classification approach for multi-view regression.  ... 
arXiv:1610.04668v1 fatcat:4wjs36nsmvherfnigqsbecf4u4

Image Aesthetics Assessment Using Graph Attention Network [article]

Koustav Ghosal, Aljosa Smolic
2022 arXiv   pre-print
But, incorporating these into the traditional convolution-based frameworks for the task of image aesthetics assessment is problematic.  ...  In this work, we present a two-stage framework based on graph neural networks and address both these problems jointly.  ...  Such rank information is especially important for the borderline images i.e. score close to 5, most of which get miss-classified as 1 in the case of binary classification.  ... 
arXiv:2206.12869v2 fatcat:azvaedgvuzgglpjibo5fsadktm

Bilateral Two-Dimensional Matrix Regression Preserving Discriminant Embedding for Corrupted Image Recognition

Jianbo Zhang, Jinkuan Wang, Mingwei Li
2019 IEEE Access  
Nuclear-norm-based matrix regression (NMR) methods have been successfully applied for the recognition of corrupted images.  ...  INDEX TERMS Corrupted image, face recognition, low-rank, matrix regression, nuclear-norm.  ...  The representative methods are linear regression based classification (LRC) [1] , sparse representation based classification (SRC) [2] and collaborative representation based classification (CRC) [3  ... 
doi:10.1109/access.2019.2892955 fatcat:jv72a4cq5vdkznkvwgm6nlug4m

Spectral Regression dimension reduction for multiple features facial image retrieval

Bailing Zhang, Yongsheng Gao
2012 International Journal of Biometrics (IJBM)  
A 98% rank 1 accuracy was obtained for the AR faces and 92% for the FERET faces.  ...  The problem of large dimensionalities of the extracted features was addressed by employing a manifold learning method called Spectral Regression (SR).  ...  Then, the significance of applying a novel Graph-Laplacian-based dimensionality reduction method, called Spectral Regression (SR), is emphasised.  ... 
doi:10.1504/ijbm.2012.044296 fatcat:rxf6pkllfvbzjjwolkcx3facbi

A Survey on Multi-label Classification for Images

Radhika Devkar, Sankirti Shiravale
2017 International Journal of Computer Applications  
Finally, paper is concluded towards challenges in multi-label classification for images for future research.  ...  In multi-label classification, each instance is assigned to multiple classes; it is a common problem in data analysis.  ...  [23] , discuss useful multi-label classification techniques for image and study clustering based multi-label classification (CBMLC) for the multi-label classification problem. Xin Li et al.  ... 
doi:10.5120/ijca2017913398 fatcat:ogsxomnv2fet3pmio5tbxym3fe

Plus Disease in Retinopathy of Prematurity

Jayashree Kalpathy-Cramer, J. Peter Campbell, Deniz Erdogmus, Peng Tian, Dharanish Kedarisetti, Chace Moleta, James D. Reynolds, Kelly Hutcheson, Michael J. Shapiro, Michael X. Repka, Philip Ferrone, Kimberly Drenser (+45 others)
2016 Ophthalmology (Rochester, Minn.)  
the i-ROP computer-based image analysis system.  ...  Methods-Images in both databases were ranked by average disease classification (classification ranking) and by pairwise comparison using the Elo rating method (comparison ranking), and correlation with  ...  In the first scenario ("classification ranking"), images were ranked in severity based on the average diagnostic classification (plus, pre-plus, or normal) by 8 experts.  ... 
doi:10.1016/j.ophtha.2016.07.020 pmid:27566853 pmcid:PMC5077696 fatcat:d2zwfqm54neodi5sts5gid7l2i

Weakly Supervised Graph Convolutional Neural Network for Human Action Localization

Daisuke Miki, Shi Chen, Kazuyuki Demachi
2020 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)  
Our weakly supervised training is based on multiple-instance learning inspired by deep ranking, and we devise a loss function so that high scores can be spontaneously learned for temporally important time  ...  In this paper, we first explain the network architecture and then present a multiple-instance learning method for its optimization.  ...  Acknowledgments This work was supported by Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research Grant Numbers JP19K20310.  ... 
doi:10.1109/wacv45572.2020.9093551 dblp:conf/wacv/MikiCD20 fatcat:vj67gbtwnbcq5jquxrxlaaixyi

Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learning [article]

Jiamiao Xu, Fangzhao Wang, Qinmu Peng, Xinge You, Shuo Wang, Xiao-Yuan Jing, C. L. Philip Chen
2020 arXiv   pre-print
Low-rank Multi-view Subspace Learning (LMvSL) has shown great potential in cross-view classification in recent years.  ...  To alleviate such limitation, modal regression is elegantly incorporated into the framework of SLMR (term it MR-SLMR).  ...  CONCLUSION In this paper, inspired by the block-diagonal representation learning and modal regression, we present a novel Modal Regression based Structured Low-rank Matrix Recovery for cross-view classification  ... 
arXiv:2003.09799v1 fatcat:x6nmrupvh5brxksmroxygpbdn4


Mirjana Ivanovic, Milos Radovanovic, Vladimir Kurbalija
2022 Computer Science and Information Systems  
"Entropy-based Network Traffic Anomaly Classification Method Resilient to Deception" authored by Juma A.  ...  anomaly detection approach with anomaly classification.  ...  The article "A Graph-based Feature Selection Method for Learning to Rank Using Spectral Clustering for Redundancy Minimization and Biased PageRank for Relevance Analysis" by Jen-Yuan Yeh and Cheng-Jung  ... 
doi:10.2298/csis220100ii fatcat:kn7lu3g6svg7tiqp2c7e6tcv2y
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