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Learned Multi-Patch Similarity [article]

Wilfried Hartmann, Silvano Galliani, Michal Havlena, Luc Van Gool, Konrad Schindler
2017 arXiv   pre-print
Experiments on several multi-view datasets demonstrate that this approach has advantages over methods based on pairwise patch similarity.  ...  Encouraged by the success of machine learning, and in particular convolutional neural networks, we propose to learn a matching function which directly maps multiple image patches to a scalar similarity  ...  Patch Similarity Learning. With the rise of machine learning for computer vision problems, it has also been proposed to learn the similarity measure for (two-view) stereo.  ... 
arXiv:1703.08836v2 fatcat:b5nfngmdarhibfnw3eb72qagmq

Patch Based Deep Local Feature Learning and Self Similarity Multi Level Clustering for Neonatal Brain Segmentation in MR Images

2019 International journal of recent technology and engineering  
To capture the features from the pre-processed image, this project offers a new technique for retrieving features called Patch Based Deep Local Feature Learning (PBDLFL).  ...  Among several supervised segmentation scheme this works employs proposed approach named Self Similarity Multi Level Clustering (SSMLC).  ...  From the various machine learning approaches this work takes only Self Similarity Multi Level Clustering.  ... 
doi:10.35940/ijrte.d9579.118419 fatcat:hr4g54higbdxhe65s3fpjgkhyy

Learned Multi-Patch Similarity

Wilfried Hartmann, Silvano Galliani, Michal Havlena, Luc Van Gool, Konrad Schindler
2017
Experiments on several multi-view datasets demonstrate that this approach has advantages over methods based on pairwise patch similarity.  ...  Encouraged by the success of machine learning, and in particular convolutional neural networks, we propose to learn a matching function which directly maps multiple image patches to a scalar similarity  ...  Plane sweeping using three different patch similarity measures. Proposed learned multi-view similarity vs. pairwise ZNCC and pairwise LIFT. Reference images (a-d).Ground truth (e-h).  ... 
doi:10.3929/ethz-b-000215952 fatcat:vgjh3dp3traivaxvpm7sjrsz2q

Deep similarity learning for multimodal medical images

Xi Cheng, Li Zhang, Yefeng Zheng
2016 Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization  
In this work, we propose a novel deep similarity learning method that trains a binary classifier to learn the correspondence of two image patches.  ...  Therefore, approaches of learning a similarity metric are proposed in recent years.  ...  Methods Similarity Metric Learning We want to learn a function f (x 1 , x 2 ) to compute a similarity score between a pair of patches x 1 and x 2 from different image modalities.  ... 
doi:10.1080/21681163.2015.1135299 fatcat:uzc4rnc4xbdmfdrgvqbilbrdsu

Learning similarity measure for multi-modal 3D image registration

Daewon Lee, Matthias Hofmann, Florian Steinke, Yasemin Altun, Nathan D. Cahill, Bernhard Scholkopf
2009 2009 IEEE Conference on Computer Vision and Pattern Recognition  
To this end, we develop an algorithm derived from max-margin structured output learning, and employ the learned similarity measure within a standard rigid registration algorithm.  ...  Instead of using a universal, but a priori fixed similarity criterion such as mutual information, we propose learning a similarity measure in a discriminative manner such that the reference and correctly  ...  Validating the learned similarity measure separately from multi-modal registration In this section, we first examine the learned local similarity measure between patches (2), and then we show some properties  ... 
doi:10.1109/cvpr.2009.5206840 dblp:conf/cvpr/LeeHSACS09 fatcat:4dftlgdxg5duderssc73ef2al4

Non-local Atlas-guided Multi-channel Forest Learning for Human Brain Labeling [chapter]

Guangkai Ma, Yaozong Gao, Guorong Wu, Ligang Wu, Dinggang Shen
2015 Lecture Notes in Computer Science  
In particular, we employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and the target labels (i.e., corresponding to certain anatomical structures).  ...  Moreover, to accommodate the high inter-subject variations, we further extend our learning-based label fusion to a multi-atlas scenario, i.e., we train a random forest for each atlas and then obtain the  ...  Instead of pair-wisely estimating the patch-based similarity, Wu et al.  ... 
doi:10.1007/978-3-319-24574-4_86 pmid:26942235 pmcid:PMC4773030 fatcat:2ghorxv6frgmre3gfvar3im4um

Brain segmentation based on multi-atlas guided 3D fully convolutional network ensembles [article]

Jiong Wu, Xiaoying Tang
2019 arXiv   pre-print
To reduce over-fitting of the FCN model on the training data, we adopted an ensemble strategy in the learning procedure.  ...  One major limitation of existing state-of-the-art 3D FCN segmentation models is that they often apply image patches of fixed size throughout training and testing, which may miss some complex tissue appearance  ...  In the multi-encoding part, the training image patch and the most similar atlas patches are separately learned.  ... 
arXiv:1901.01381v1 fatcat:fbuq7fbsjfcqtm4d5ld7oxq5ey

Deep Multi-Spectral Registration Using Invariant Descriptor Learning [article]

Nati Ofir, Shai Silberstein, Hila Levi, Dani Rozenbaum, Yosi Keller, Sharon Duvdevani Bar
2018 arXiv   pre-print
Our algorithm detects corners by Harris and matches them by a patch-metric learned on top of CIFAR-10 network descriptor.  ...  Multi-modal images of the same scene capture different signals and therefore their registration is challenging and it is not solved by classic approaches.  ...  Given a VIS channel patch P v and a NIR patch P n we offer to learn a metric that measures the similarity distance between them.  ... 
arXiv:1801.05171v6 fatcat:4aqaegtcgbcotf7wdyeom6e4zu

The Multi-level Learning and Classification of Multi-class Parts-Based Representations of U.S. Marine Postures [chapter]

Deborah Goshorn, Juan Wachs, Mathias Kölsch
2009 Lecture Notes in Computer Science  
The first approach uses a two-level learning method which consists of simple clustering of interest patches extracted from a set of training images for each posture, in addition to learning the nonparametric  ...  This paper primarily investigates the possibility of using multi-level learning of sparse parts-based representations of US Marine postures in an outside and often crowded environment for training exercises  ...  The first classifier presented in this paper is a multi-class version similar in learning, but different in classification to the pedestrian detector in [5] .  ... 
doi:10.1007/978-3-642-10268-4_59 fatcat:5o4ntduu3zfldowte6ri7ncv6u

mc-BEiT: Multi-choice Discretization for Image BERT Pre-training [article]

Xiaotong Li, Yixiao Ge, Kun Yi, Zixuan Hu, Ying Shan, Ling-Yu Duan
2022 arXiv   pre-print
further refined by high-level inter-patch perceptions resorting to the observation that similar patches should share their choices.  ...  Specifically, the multi-choice supervision for the masked image patches is formed by the soft probability vectors of the discrete token ids, which are predicted by the off-the-shelf image tokenizer and  ...  similarities and form ensembled learning targets for masked image patches (see Fig. 2 ).  ... 
arXiv:2203.15371v2 fatcat:kype74wae5esbfg2oc32wu7pxa

Metric Learning for Multi-atlas based Segmentation of Hippocampus

Hancan Zhu, Hewei Cheng, Xuesong Yang, Yong Fan
2016 Neuroinformatics  
The learned distance metric model is then used to compute the similarity measure between image patches in the label fusion.  ...  In this paper, we proposed a novel metric learning method to fuse segmentation labels in multi-atlas based image segmentation.  ...  Conclusion In the paper, we propose a novel nonlocal patch based weighted voting label fusion method with a learned distance metric for measuring similarity between image patches.  ... 
doi:10.1007/s12021-016-9312-y pmid:27638650 pmcid:PMC5438876 fatcat:ihxneblsjzacjdh67qbfraqe3q

Joint patch and multi-label learning for facial action unit detection

Kaili Zhao, Wen-Sheng Chu, Fernando De la Torre, Jeffrey F. Cohn, Honggang Zhang
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
We introduce joint-patch and multi-label learning (JPML) to address these issues. JPML leverages group sparsity by selecting a sparse subset of facial patches while learning a multi-label classifier.  ...  These are patch and multi-label learning.  ...  Given such AU relations, we develop joint patch and multi-label learning (JPML) to simultaneously select a discriminative subset of patches and learn multi-AU classifiers.  ... 
doi:10.1109/cvpr.2015.7298833 pmid:27382243 pmcid:PMC4930865 dblp:conf/cvpr/ZhaoCTCZ15 fatcat:oc27rkscsjclfcyxib5eipbodu

Patch-Based Identification of Lexical Semantic Relations [chapter]

Nesrine Bannour, Gaël Dias, Youssef Chahir, Houssam Akhmouch
2020 Lecture Notes in Computer Science  
For that purpose, we present different binary and multi-task classification strategies that include two distinct attention mechanisms based on PageRank.  ...  . − → w i (7) Learning Framework In order to perform binary and multi-task classification, we define two distinct learning input representations, X p and X s , which combine patches representations,  ...  So, multi-task learning strategies have been proposed [1] , which concurrently learn different semantic relations with the assumption that the learning process of a given semantic relation may be improved  ... 
doi:10.1007/978-3-030-45439-5_9 fatcat:novvomhhevhhzb53vgns7i4qs4

Brain Image Labeling Using Multi-atlas Guided 3D Fully Convolutional Networks [chapter]

Longwei Fang, Lichi Zhang, Dong Nie, Xiaohuan Cao, Khosro Bahrami, Huiguang He, Dinggang Shen
2017 Lecture Notes in Computer Science  
Specifically, multi-atlas based guidance is incorporated during the network learning. Based on this, the discriminative of the FCN is boosted, which eventually contribute to accurate prediction.  ...  Alternatively, the patch-based methods have been proposed to relax the requirement of image registration, but the labeling is often determined independently by the target image information, without getting  ...  those similar atlas patches.  ... 
doi:10.1007/978-3-319-67434-6_2 pmid:29104969 pmcid:PMC5669261 fatcat:mdat4oofqzghnjhkinevbjpp2y

Learning Correspondence Structures for Person Re-identification [article]

Weiyao Lin, Yang Shen, Junchi Yan, Mingliang Xu, Jianxin Wu, Jingdong Wang, Ke Lu
2017 arXiv   pre-print
Finally, we also extend our approach by introducing a multi-structure scheme, which learns a set of local correspondence structures to capture the spatial correspondence sub-patterns between a camera pair  ...  We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair.  ...  ) can achieve similar performances to the one under manually divided pose group pairs (proposed+multi-manu).  ... 
arXiv:1703.06931v3 fatcat:honmcoh67va5fozfl6op6scknu
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