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RSSGG_CS: Remote Sensing Image Scene Graph Generation by Fusing Contextual Information and Statistical Knowledge

Zhiyuan Lin, Feng Zhu, Qun Wang, Yanzi Kong, Jianyu Wang, Liang Huang, Yingming Hao
2022 Remote Sensing  
Therefore, we propose a novel model for remote sensing image scene graph generation by fusing contextual information and statistical knowledge, namely RSSGG_CS.  ...  Experiments show that fusing contextual information and statistical knowledge allows the model to generate more complete scene graphs of remote sensing images and facilitates the semantic understanding  ...  Statistical knowledge as prior information greatly reduces the blindness of the model when searching the semantic space while making it easier for the model to generate high-frequency predicates.  ... 
doi:10.3390/rs14133118 fatcat:qx3hwzrulfdk7m7ikbsvibqy5u

Research on Optimal Data Selection Technology of Optical Remote Sensing Satellite Images

Peng Zhang, School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou 510006, China
2020 Journal of Communications  
The method uses the image semantic segmentation technology to achieve refined cloud detection, uses the various indicator comprehensively evaluating the image quality, uses the regional coverage technology  ...  This paper realizes a data selection method for optical remote sensing satellite images.  ...  It is divided into three parts: semantic segmentation dataset, semantic segmentation network and cloud detection. 1) Semantic segmentation dataset for cloud detection Image semantic segmentation based  ... 
doi:10.12720/jcm.15.2.185-191 fatcat:yzc6kpinyngvvdv2uzp3mn5qnm

Automatic Image Annotation Using Maximum Entropy Model [chapter]

Wei Li, Maosong Sun
2005 Lecture Notes in Computer Science  
In the phase of training, a basic visual vocabulary consisting of blob-tokens to describe the image content is generated at first; then the statistical relationship is modeled between the blob-tokens and  ...  Automatic image annotation is a newly developed and promising technique to provide semantic image retrieval via text descriptions.  ...  Section 5 presents conclusions and a comment for future work. Related Work Recently, many statistical models have been proposed for automatic image annotation and retrieval.  ... 
doi:10.1007/11562214_4 fatcat:4mkfcthddbcdfchkvsxynczywi

Generalizable Model-agnostic Semantic Segmentation via Target-specific Normalization [article]

Jian Zhang, Lei Qi, Yinghuan Shi, Yang Gao
2021 arXiv   pre-print
To this end, we propose a novel domain generalization framework for the generalizable semantic segmentation task, which enhances the generalization ability of the model from two different views, including  ...  Extensive experiments highlight that the proposed method produces state-of-the-art performance for the domain generalization of semantic segmentation on multiple benchmark segmentation datasets, i.e.,  ...  We provide a strong baseline for semantic segmentation DG problem.  ... 
arXiv:2003.12296v2 fatcat:t65rdkl6r5drhgiywrmer73qa4

Development of Coral Investigation System Based on Semantic Segmentation of Single-Channel Images

Hong Song, Syed Raza Mehdi, Yangfan Zhang, Yichun Shentu, Qixin Wan, Wenxin Wang, Kazim Raza, Hui Huang
2021 Sensors  
networks for semantic segmentation.  ...  Based on single-channel images, a convolutional neural network (CNN) model, named DeeperLabC, is employed for the semantic segmentation of corals, which is a concise and modified deeperlab model with encoder-decoder  ...  dataset collection, and their helpful feedback and discussion throughout the development of the model.  ... 
doi:10.3390/s21051848 pmid:33800839 pmcid:PMC7961541 fatcat:oywfdafw6nc6hbcrg6uppebrgi

Context Based Visual Content Verification [article]

Martin Lukac, Aigerim Bazarbayeva, Michitaka Kameyama
2017 arXiv   pre-print
In order to train our model we provide new annotated dataset the Advanced Attribute VOC (AAVOC) that contains additional properties of the image.  ...  The co-occurrence statistics are in general used to determine relational properties between objects based on information collected from data.  ...  Consequently co-occurrence statistics are very useful and can be used both for verification as well as for model generation. III.  ... 
arXiv:1709.00141v1 fatcat:36ci4caixnf2jgafzvxij3byne

Modelling semantic context for novelty detection in wildlife scenes

Suet-Peng Yong, Jeremiah D. Deng, Martin K. Purvis
2010 2010 IEEE International Conference on Multimedia and Expo  
The semantic co-occurrence matrices then undergo binarization and principal component analysis for dimension reduction, forming the basis for constructing one-class models for each scene category.  ...  Working with wildlife image data, the framework starts with image segmentation, followed by feature extraction and classification of the image blocks extracted from image segments.  ...  For semantic analysis of new images, we have found it is more effective to train classifiers on segment blocks instead of on segment images directly.  ... 
doi:10.1109/icme.2010.5583899 dblp:conf/icmcs/YongDP10 fatcat:l27bcyg4j5gl5bogxakrg6peaq

Image region annotation based on segmentation and semantic correlation analysis

Jing Zhang, Yakun Mu, Shengwei Feng, Kehuang Li, Yubo Yuan, Chin-Hui Lee
2018 IET Image Processing  
The authors propose an image region annotation framework by exploring syntactic and semantic correlations among segmented regions in an image.  ...  A texture-enhanced image segmentation JSEG algorithm is first used to improve the pixel consistency in a segmented image region.  ...  There are mainly two kinds of statistical models [4] .  ... 
doi:10.1049/iet-ipr.2017.0917 fatcat:uitqykfenbbtlpbcj6j4ue3fly

Optimal solutions for semantic image decomposition

Daniel Cremers
2012 Image and Vision Computing  
a r t i c l e i n f o Keywords: Optimization Efficient algorithms Convexity Semantic labeling Bridging the gap between low-level and high-level image analysis has been a central challenge in computer vision  ...  Moreover, higher-level semantic knowledge can be learned and imposed on the basis of such multi-label formulations.  ...  Input Segmentation [9] Fig. 2. Semantic segmentation obtained using label co-occurrence statistics. Input Segmentation [14] Fig. 3. Semantic segmentation obtained using ordering constraints.  ... 
doi:10.1016/j.imavis.2011.12.011 fatcat:pri6sdh6gndm7bmtdw3k4jppv4

SEMANTIC SEGMENTATION METHOD ACCELERATED QUANTITATIVE ANALYSIS OF THE SPATIAL CHARACTERISTICS OF TRADITIONAL VILLAGES

M. Zhang, Z. Li, X. Wu
2021 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Semantic segmentation is a computer vision technique for quickly segmenting different objects.  ...  Using Transfer learning, data augmentation and other methods, a model was trained that can automatically segment elements of the villages.  ...  A semantic segmentation dataset for Chinese traditional villages was built based on remote sensing images, and the OCR-Net model was used to train a model that can automatically segment remote sensing  ... 
doi:10.5194/isprs-archives-xlvi-m-1-2021-933-2021 fatcat:5ynmjctebjbixh4456xkwmxitu

Weakly supervised semantic segmentation for social images

Wei Zhang, Sheng Zeng, Dequan Wang, Xiangyang Xue
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Image semantic segmentation is the task of partitioning image into several regions based on semantic concepts.  ...  In this paper, we learn a weakly supervised semantic segmentation model from social images whose labels are not pixellevel but image-level; furthermore, these labels might be noisy.  ...  In this paper, we propose a weakly supervised semantic segmentation model to overcome the challenge posed by noisy image-level labels for training.  ... 
doi:10.1109/cvpr.2015.7298888 dblp:conf/cvpr/ZhangZWX15 fatcat:zjwopqvpv5e3jgeh5thfim7cmm

A bag of words approach for semantic segmentation of monitored scenes

Wassim Bouachir, Atousa Torabi, Guillaume-Alexandre Bilodeau, Pascal Blais
2016 2016 International Symposium on Signal, Image, Video and Communications (ISIVC)  
This paper proposes a semantic segmentation method for outdoor scenes captured by a surveillance camera.  ...  information with the image keypoints.  ...  Generally, the most prevalent model used for image segmentation is the Markov Random Field [9] - [14] .  ... 
doi:10.1109/isivc.2016.7893967 dblp:conf/isivc/BouachirTBB16 fatcat:bg4f2kg6hfa6renpg7e62oilo4

A Bag of Words Approach for Semantic Segmentation of Monitored Scenes [article]

Wassim Bouachir, Atousa Torabi, Guillaume-Alexandre Bilodeau, Pascal Blais
2013 arXiv   pre-print
This paper proposes a semantic segmentation method for outdoor scenes captured by a surveillance camera.  ...  information with the image keypoints.  ...  Generally, the most prevalent model used for image segmentation is the Markov Random Field [9] - [14] .  ... 
arXiv:1305.3189v1 fatcat:rhxgqma4kfdediw374su5huhhm

Unsupervised Hierarchical Semantic Segmentation with Multiview Cosegmentation and Clustering Transformers [article]

Tsung-Wei Ke, Jyh-Jing Hwang, Yunhui Guo, Xudong Wang, Stella X. Yu
2022 arXiv   pre-print
Existing methods avoid this ambiguity and treat it as a factor outside modeling, whereas we embrace it and desire hierarchical grouping consistency for unsupervised segmentation.  ...  Unsupervised semantic segmentation aims to discover groupings within and across images that capture object and view-invariance of a category without external supervision.  ...  Unsupervised semantic segmentation has been modeled by non-parametric methods using statistical features and graphical models [39, 49, 54] .  ... 
arXiv:2204.11432v1 fatcat:kqhf6u7w4jgnnicm2z2nvwzezu

Robust high-throughput phenotyping with deep segmentation enabled by a web-based annotator [article]

Jialin Yuan, Damanpreet Kaur, Zheng Zhou, Michael Nagle, Nihar A. Doshi, Ali Behnoudfar, Ekaterina Peremyslova, Cathleen Ma, Steven H. Strauss, Fuxin Li
2022 bioRxiv   pre-print
Our evaluation shows that our proposed SGIOS model requires fewer user inputs compared to the state-of-art models for interactive segmentation.  ...  We propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS).  ...  We thank Middleton Spectral Vision (Wisconsin) for their customized imaging system used to capture regeneration data, and high quality imaging system support.  ... 
doi:10.1101/2022.03.11.483823 fatcat:k4nxbwlg2fbbxdhubodxtstofi
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