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Structured max-margin learning for multi-label image annotation

Xiangyang Xue, Hangzai Luo, Jianping Fan
2010 Proceedings of the ACM International Conference on Image and Video Retrieval - CIVR '10  
In this paper, a structured max-margin learning scheme is developed to achieve more effective training of a large number of inter-related classifiers for multi-label image annotation.  ...  Third, a structured max-margin learning algorithm is developed by incorporating the visual concept network, maxmargin Markov networks and multi-task learning to address the issue of huge inter-concept  ...  Figure 4 : 4 Multi-label image annotation results. Figure 5 : 5 The average precision and recall rates of our structured max-margin learning algorithm for 1000 objects and image concepts.  ... 
doi:10.1145/1816041.1816056 dblp:conf/civr/XueLF10 fatcat:zmhoybpwo5drjlxi577nbyaawq

Tile-Level Annotation of Satellite Images Using Multi-Level Max-Margin Discriminative Random Field

Fan Hu, Wen Yang, Jiayu Chen, Hong Sun
2013 Remote Sensing  
This paper proposes a multi-level max-margin discriminative analysis (M 3 DA) framework, which takes both coarse and fine semantics into consideration, for the annotation of high-resolution satellite images  ...  Moreover, for improving the spatial coherence of visual words neglected by M 3 DA, conditional random field (CRF) is employed to optimize the soft label field composed of multiple label posteriors.  ...  The authors would like to specially thank Kan Xu for his helpful guidance on MedLDA. Conflict of Interest The authors declare no conflict of interest. Remote Sens. 2013, 5  ... 
doi:10.3390/rs5052275 fatcat:llmbvgc7lregva3ofjzeytowre

Correlative multi-label multi-instance image annotation

Xiangyang Xue, Wei Zhang, Jie Zhang, Bin Wu, Jianping Fan, Yao Lu
2011 2011 International Conference on Computer Vision  
Structural max-margin technique is used to formulate the proposed model and multiple interrelated classifiers are learned jointly.  ...  A novel method is developed for achieving multi-label multi-instance image annotation, where image-level (bag-level) labels and region-level (instance-level) labels are both obtained.  ...  Acknowledgments We would like to thank the anonymous reviewers for their helpful comments. This work was supported in part by  ... 
doi:10.1109/iccv.2011.6126300 dblp:conf/iccv/XueZZWFL11 fatcat:r5cqzgb6szb7vigagle7prs4fe

Predictive Subspace Learning for Multi-view Data: a Large Margin Approach

Ning Chen, Jun Zhu, Eric P. Xing
2010 Neural Information Processing Systems  
Learning from multi-view data is important in many applications, such as image classification and annotation.  ...  Finally, we demonstrate the advantages of large-margin learning on real video and web image data for discovering predictive latent representations and improving the performance on image classification,  ...  Chen was a visiting researcher at CMU under a CSC fellowship and supports from Chinese NSF Grants (No. 60625304, 90716021, 61075027), the National Key Project for Basic Research of China (Grants No.  ... 
dblp:conf/nips/ChenZX10 fatcat:lb6qxq2pfbhb5a5cmdmuldfsxi

Active Boundary Annotation using Random MAP Perturbations

Subhransu Maji, Tamir Hazan, Tommi S. Jaakkola
2014 International Conference on Artificial Intelligence and Statistics  
We address the problem of efficiently annotating labels of objects when they are structured.  ...  in a multi-scale manner.  ...  Annotations for complex mod- els are described by structured-labels, e.g., a sequence of labels that are strongly correlated. Specifically, image annotations provide a semantic label for each pixel.  ... 
dblp:conf/aistats/MajiHJ14 fatcat:52cr3nmigbdpfc3c5wexnlbwpi

Self-supervised asymmetric deep hashing with margin-scalable constraint [article]

Zhengyang Yu, Song Wu, Zhihao Dou, Erwin M.Bakker
2021 arXiv   pre-print
methods are based upon an oversimplified similarity assignment(i.e., 0 for instance pairs sharing no label, 1 for instance pairs sharing at least 1 label), 2) the exploration in multi-semantic relevance  ...  However, it is still challenging to produce compact and discriminative hash codes for images associated with multiple semantics for two main reasons, 1) similarity constraints designed in most of the existing  ...  Program for Chongqing Overseas Returnees (CX2018075).  ... 
arXiv:2012.03820v3 fatcat:fscm4ggdyrct3o6kso53mmriou

Max-margin Latent Dirichlet Allocation for Image Classification and Annotation

Yang Wang, Greg Mori
2011 Procedings of the British Machine Vision Conference 2011  
We present the max-margin latent Dirichlet allocation, a max-margin variant of supervised topic models, for image classification and annotation.  ...  Our model for image annotation (called MMLDA a ) extends MMLDA c to the case of multi-label problems, where each image can be associated with more than one annotation terms.  ...  It is therefore desirable to combine topic models with max-margin learning.  ... 
doi:10.5244/c.25.112 dblp:conf/bmvc/0003M11 fatcat:txjt5butkjhqtjd4x2abn656mm

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.  ...  The area of an image multi-label classification is increase continuously in last few years, in machine learning and computer vision.  ...  [14] , proposed a framework in which multiple labels are obtain by using feature label association and inter label correlation. Co-occurrence matrix and structured max margin framework is used.  ... 
doi:10.5120/ijca2017913398 fatcat:ogsxomnv2fet3pmio5tbxym3fe

Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification

Lei Liu, Wentao Lei, Xiang Wan, Li Liu, Yongfang Luo, Cheng Feng
2020 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)  
The core component of TSAL is the multi-label learning mechanism, in which label correlation information is used to design a multi-label margin (MLM) strategy and a confidence validation for automatically  ...  However, due to complex feature behaviors and expensive annotations of US images, it is difficult to apply Artificial Intelligence (AI) assisting approaches for the lung's multi-symptom (multi-label) classification  ...  Thus, this margin intuitively can measure the informativeness of the unlabeled images. 2) Label stream: Confidence validation: Label correlations information has been widely employed for multi-label learning  ... 
doi:10.1109/ictai50040.2020.00191 fatcat:aixm52wfybe4ngb4umnjk4gjqe

Denoising of a Mixed Noise Color Image through Special Filter

Sandeep Kumar Agarwal, Prateek Kumar
2015 International Journal of Big Data Security Intelligence  
Most studies solid image annotation into a multi-label classification downside.  ...  With associate degree increasing range of pictures that are on the market in social media, image annotation has emerged as a very important analysis topic attributable to its application in image matching  ...  In, a max-margin riffled independence model is developed for tag ranking.  ... 
doi:10.21742/ijbdsi.2015.2.2.03 fatcat:sihvwvy4kjaezmpnk4l3qhdwqe

Objective-Guided Image Annotation

Qi Mao, Ivor Wai-Hung Tsang, Shenghua Gao
2013 IEEE Transactions on Image Processing  
Index Terms-Image annotation, multi-label learning, performance measures, structural support vector machine (SVM).  ...  Specifically, we first present a multilayer hierarchical structure of learning hypotheses for multi-label problems based on which a variety of loss functions with respect to objectiveguided measures are  ...  methods on benchmark datasets for multi-label learning and the popular benchmark datasets for image annotation.  ... 
doi:10.1109/tip.2012.2233490 pmid:23247859 fatcat:4b4ki4mckng4patyqubf3vbln4

Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection [article]

Meng Ye, Yuhong Guo
2018 arXiv   pre-print
, while simultaneously learning a max-margin multi-label classifier with the projected label embeddings.  ...  We conduct experiments for zero-shot multi-label image classification. The results demonstrate the efficacy of the proposed approach.  ...  The projection matrices are learnt under a max-margin multi-label learning framework based on the matching scores of the images and labels in the projected semantic space.  ... 
arXiv:1808.02474v1 fatcat:dov2w7ofbvdg3kdfiprkb5sm3i

A Correlation Approach for Automatic Image Annotation [chapter]

David R. Hardoon, Craig Saunders, Sandor Szedmak, John Shawe-Taylor
2006 Lecture Notes in Computer Science  
In this work we experiment with semantic models and multi-class learning for the automatic annotation of query images.  ...  The automatic annotation of images presents a particularly complex problem for machine learning researchers.  ...  There is a strong demand for extending the underlying idea towards multi-class classification and learning when the outputs have complex structure.  ... 
doi:10.1007/11811305_75 fatcat:g3tey6jalnhz7ctigdpywhve5q

Transductive Kernel Map Learning and Its Application Image Annotation

Phong Vo, Hichem Sahbi
2012 Procedings of the British Machine Vision Conference 2012  
We introduce in this paper a novel image annotation approach based on maximum margin classification and a new class of kernels.  ...  and a learned kernel map ii) a fidelity term that ensures consistent label predictions with those provided in a training set and iii) a smoothness term which guarantees similar labels for neighboring  ...  Max Margin Inference for Multi-label Classification The general classification problem aims to learn a classifier f , that minimizes training error and also generalize well on test data, as argmin f R(  ... 
doi:10.5244/c.26.68 dblp:conf/bmvc/VoS12 fatcat:3ooxwgh6irey7lntc22oy3dk24

Fully Convolutional Multi-Class Multiple Instance Learning [article]

Deepak Pathak, Evan Shelhamer, Jonathan Long, Trevor Darrell
2015 arXiv   pre-print
In this setting, we seek to learn a semantic segmentation model from just weak image-level labels.  ...  Multiple instance learning (MIL) can reduce the need for costly annotation in tasks such as semantic segmentation by weakening the required degree of supervision.  ...  We incorporate multi-class annotations by making multi-class inferences for each image.  ... 
arXiv:1412.7144v4 fatcat:2jyzciesorem5ncvnbo6bkjtiu
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