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Structured max-margin learning for multi-label image annotation
2010
Proceedings of the ACM International Conference on Image and Video Retrieval - CIVR '10
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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
2013
Remote Sensing
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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
2011
2011 International Conference on Computer Vision
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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
2010
Neural Information Processing Systems
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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
2014
International Conference on Artificial Intelligence and Statistics
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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]
2021
arXiv
pre-print
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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
2011
Procedings of the British Machine Vision Conference 2011
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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
2017
International Journal of Computer Applications
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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
2020
2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)
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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
2015
International Journal of Big Data Security Intelligence
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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
2013
IEEE Transactions on Image Processing
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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]
2018
arXiv
pre-print
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, 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]
2006
Lecture Notes in Computer Science
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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
2012
Procedings of the British Machine Vision Conference 2012
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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]
2015
arXiv
pre-print
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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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