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Semi-supervised Ranking for Object Image Blur Assessment [article]

Qiang Li, Zhaoliang Yao, Jingjing Wang, Ye Tian, Pengju Yang, Di Xie, Shiliang Pu
2022 arXiv   pre-print
Assessing the blurriness of an object image is fundamentally important to improve the performance for object recognition and retrieval.  ...  face image blur assessment dataset with reliable labels.  ...  object image quality, and solve one of the most important factors assessment, i.e. the object image blur assessment.  ... 
arXiv:2207.06085v1 fatcat:dplgayndrzhcjndtavtfx2dumq

Self-supervised blur detection from synthetically blurred scenes

Aitor Alvarez-Gila, Adrian Galdran, Estibaliz Garrote, Joost van de Weijer
2019 Image and Vision Computing  
or semi-supervised configurations.  ...  In this work, we bypass the need for such annotated datasets for end-to-end learning, and instead rely on object proposals and a model for blur generation in order to produce a dataset of synthetically  ...  In order to assess the usefulness of this semi-supervised setting, we consider the 400 images from Shi et al .'  ... 
doi:10.1016/j.imavis.2019.08.008 fatcat:nhmy5okor5h35br4kaidevq6eq

AQuA: Analytical Quality Assessment for Optimizing Video Analytics Systems [article]

Sibendu Paul, Utsav Drolia, Y. Charlie Hu, Srimat T. Chakradhar
2021 arXiv   pre-print
When used for filtering poor quality frames at edge, it reduces high-confidence errors for analytics applications by 17%.  ...  It takes into account the analytical quality of frames, not the visual quality, by learning a novel metric, classifier opinion score, and uses a lightweight, CNN-based, object-independent feature extractor  ...  Moreover, the generalization of AQuA is boosted by semi-supervision because it accounts for the frame and the appearance of the object inside the frame, rather than just considering the specific object  ... 
arXiv:2101.09752v2 fatcat:z7qi7bmhlna4hijompouhq4y24

Reference-free particle selection enhanced with semi-supervised machine learning for cryo-electron microscopy

Robert Langlois, Jesper Pallesen, Joachim Frank
2011 Journal of Structural Biology  
The method is augmented with a new semi-supervised machine-learning algorithm to accurately discriminate particles from contaminants and noise.  ...  We would also like to thank Hstau Liao for insightful discussions and Melissa Thomas for the illustrations.  ...  Then, for each object radius, a Gaussian blur of the previous blur is calculated (scaled by the DoG width), and the difference is taken between both the current and previous blurs.  ... 
doi:10.1016/j.jsb.2011.06.004 pmid:21708269 pmcid:PMC3205936 fatcat:ay5d37jm7bb6pbbvawbke25mke

Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessment [article]

Yue Cao and Zhaolin Wan and Dongwei Ren and Zifei Yan and Wangmeng Zuo
2022 arXiv   pre-print
In this paper, we suggest to incorporate semi-supervised and positive-unlabeled (PU) learning for exploiting unlabeled data while mitigating the adverse effect of outliers.  ...  Full-reference (FR) image quality assessment (IQA) evaluates the visual quality of a distorted image by measuring its perceptual difference with pristine-quality reference, and has been widely used in  ...  for semi-supervised learning of IQA models.  ... 
arXiv:2204.08763v1 fatcat:2u4v2roffbcrtdcjo5s4zknwfe

Table of contents

2021 IEEE Transactions on Image Processing  
Pan Exploring the Effects of Blur and Deblurring to Visual Object Tracking .  ...  Isler Semi-Supervised Low-Rank Semantics Grouping for Zero-Shot Learning ..... B. Xu, Z. Zeng, C. Lian, and Z.  ...  Qin Cubemap-Based Perception-Driven Blind Quality Assessment for 360-degree Images .....................................  ... 
doi:10.1109/tip.2021.3129401 fatcat:jfcp3vj26vgahpbufknwbg4y2i

Near-duplicate keyframe retrieval by semi-supervised learning and nonrigid image matching

Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu, Shuicheng Yan
2011 ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)  
To attack this challenge, we employ a semi-supervised learning method, semi-supervised support vector machines, which is able to significantly improve the retrieval performance by exploiting unlabeled  ...  The promising results indicate that our proposed method is more effective than other state-of-the-art approaches for near-duplicate keyframe retrieval.  ...  Zhao for providing their experimental results. The authors also thank the reviewers and associate editor for their helpful comments.  ... 
doi:10.1145/1870121.1870125 fatcat:iq27vvj6gfe7vnoyc37zuupupm

An original framework for Wheat Head Detection using Deep, Semi-supervised and Ensemble Learning within Global Wheat Head Detection (GWHD) Dataset [article]

Fares Fourati, Wided Souidene, Rabah Attia
2020 arXiv   pre-print
We use semi supervised learning to boost previous supervised models of object detection. Moreover, we put much effort on ensemble to achieve higher performance.  ...  Our proposed method was ranked within the top 6% in the above mentioned challenge.  ...  Semi-supervised learning: Pseudo Labeling (PL) We have used a pseudo labeling [15] as our semi-supervised algorithm during the inference time.  ... 
arXiv:2009.11977v1 fatcat:lheuznt2dneolmtayfequkrjpa

Author Index

2010 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition  
Subwindow Search Verbeek, Jakob Multimodal Semi-supervised Learning for Image Classification Improving Web Image Search Results using Query-relative Classifiers Vezhnevets, Alexander Towards Weakly Supervised  ...  Object and Human Pose in Human-Object Interaction Activities Connecting Modalities: Semi-supervised Segmentation and Annotation of Images Using Unaligned Text Corpora Felzenszwalb, Pedro F.  ... 
doi:10.1109/cvpr.2010.5539913 fatcat:y6m5knstrzfyfin6jzusc42p54

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 2066-2077 Semantics-Preserving Graph Propagation for Zero-Shot Object Detection. Yan, C., +, TIP 2020 8163-8176 Semi-Supervised Image Dehazing.  ...  ., +, TIP 2020 538-550 Semi-Supervised Robust Mixture Models in RKHS for Abnormality Detection in Medical Images.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TIP 2021 5573-5588 Semi-Supervised Low-Rank Semantics Grouping for Zero-Shot Learning.  ...  ., +, TIP 2021 5573-5588 Semi-Supervised Low-Rank Semantics Grouping for Zero-Shot Learning. Dual Modulated QR Codes for Proximal Privacy and Security.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

Automatic Assessment of Artistic Quality of Photos [article]

Ashish Verma, Kranthi Koukuntla, Rohit Varma, Snehasis Mukherjee
2018 arXiv   pre-print
The proposed measurement of artistic quality of images provides higher value of photo quality for the images captured by professional photographers, compared to the values provided for the other images  ...  This paper proposes a technique to assess the aesthetic quality of photographs.  ...  Algorithms which can assess and grade the image, can be useful in ranking the image, and this image ranking algorithm can enhance the result of image based search engine, which can be useful in various  ... 
arXiv:1804.06124v1 fatcat:pxr4gdlhhvhvfjruqq2lolrj2y

Front Matter: Volume 11187

Qionghai Dai, Tsutomu Shimura, Zhenrong Zheng
2019 Optoelectronic Imaging and Multimedia Technology VI  
layered fusion and exemplar-based 11187 1V BNU-LCSAD: a video database for classroom student action recognition 11187 1W Improvement of semi-supervised learning in real application scenarios 11187  ...  quality assessment based on an objective quality database and deep neural networks 11187 09 Video quality assessment based on LOG filtering of videos and spatiotemporal slice images 11187 0A No-reference  ... 
doi:10.1117/12.2563101 fatcat:zsy4ucxvlfd4hjepdpolsjfeya

Parting with Illusions about Deep Active Learning [article]

Sudhanshu Mittal, Maxim Tatarchenko, Özgün Çiçek, Thomas Brox
2019 arXiv   pre-print
They improve by a large-margin when integrated with semi-supervised learning, but barely perform better than the random baseline.  ...  We re-implement various latest active learning approaches for image classification and evaluate them under more realistic settings. We further validate our findings for semantic segmentation.  ...  In this section, we assess the performance of state-of-theart AL methods for image classification and compare them with the state-of-the-art semi-supervised approach.  ... 
arXiv:1912.05361v1 fatcat:xmjvemvy2fgolbmrtd4skuxhvq

A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images

Igor Stępień, Mariusz Oszust
2022 Journal of Imaging  
No-reference image quality assessment (NR-IQA) methods automatically and objectively predict the perceptual quality of images without access to a reference image.  ...  In this work, a survey covering recently introduced NR-IQA methods for the assessment of MR images is presented.  ...  Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI Consistency Table 2 . 2 Details of the MR image datasets.  ... 
doi:10.3390/jimaging8060160 pmid:35735959 pmcid:PMC9224540 fatcat:7njjlij42rcpvpwb47vlon5vqm
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