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Discriminative Paired Dictionary Learning for Visual Recognition
2016
Proceedings of the 2016 ACM on Multimedia Conference - MM '16
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A Paired Discriminative K-SVD (PD-KSVD) dictionary learning method is presented in this paper for visual recognition. ...
To achieve high discrimination and low reconstruction errors simultaneously for sparse coding, we propose to learn class-specific sub-dictionaries from pairs of positive and negative classes to jointly ...
We argue that the discrimination of dictionary can be also achieved through learning from pairs of classes. ...
doi:10.1145/2964284.2967184
dblp:conf/mm/WangCC16
fatcat:j2fzfolgb5ejnm2gleibnrkk3e
Active Discriminative Dictionary Learning for Weather Recognition
2016
Mathematical Problems in Engineering
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Secondly, unlike other methods which used the traditional classifiers (e.g., SVM andK-NN), we use discriminative dictionary learning as the classification model for weather, which could address the limitations ...
Moreover, the active learning procedure is introduced into dictionary learning to avoid requiring a large number of labeled samples to train the classification model for achieving good performance of weather ...
dictionary pair for obtaining good results. ...
doi:10.1155/2016/8272859
fatcat:lcntodjirjaxpbw7nuqtujy5jq
Person Re-Identification Based on Weighted Indexing Structures
[chapter]
2014
Lecture Notes in Computer Science
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The results shown in Figure 4 (b) indicate that the predominance filter is more discriminative achieving better recognition rates. Visual Dictionaries. ...
Other works focus on data scalability of transference learning, dealing with the ability of learning a model obtained from an initial pair of cameras to adapt to a new pair of target cameras [15, 16] ...
doi:10.1007/978-3-319-12568-8_44
fatcat:zyprrmkxgvb5hfmdgxlpzbt2bi
Class-Specific Sparse Principal Component Analysis for Visual Classification
2020
IEEE Access
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First, we propose a class-specific sparse PCA approach to extend the conventional dictionary learning to dictionary pair learning for visual classification. ...
In this paper, we focus on designing capable dictionary learning architecture for the visual classification task with few-shot training samples. ...
Class-specific dictionary learning based classification method learns a dictionary for each class and the learned dictionary is discriminative. ...
doi:10.1109/access.2020.3001202
fatcat:rp6xkm5ilre4jpkvxqqq4l2s44
Unsupervised visual domain adaptation via dictionary evolution
2016
2016 IEEE International Conference on Multimedia and Expo (ICME)
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favorable to visual recognition. ...
We claim that a shared dictionary joining discriminative power with representative ability is more conductive to cross-domain visual recognition. ...
doi:10.1109/icme.2016.7552896
dblp:conf/icmcs/WuJYZYY16
fatcat:trhrhyk22feifnnxcqpaegteri
Discriminative Local Sparse Representation by Robust Adaptive Dictionary Pair Learning
[article]
2019
arXiv
pre-print
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In this paper, we propose a structured Robust Adaptive Dic-tionary Pair Learning (RA-DPL) framework for the discrim-inative sparse representation learning. ...
Specifically, RA-DPL improves existing projective dictionary pair learning in four perspectives. ...
Foundation of China (61672365, 61732008, 61725203, 61622305, 61871444, 61806035), High-Level Talent of the "Six Talent Peak" Project of Jiangsu Province (XYDXX-055) and the Fundamental Research Funds for ...
arXiv:1911.08680v1
fatcat:edcgg2flgfgurgj5wmef47vbn4
Object categorization by learned universal visual dictionary
2005
Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1
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The main contribution of this paper is two fold: i) an optimally compact visual dictionary is learned by pair-wise merging of visual words from an initially large dictionary. ...
Abstract This paper presents a new algorithm for the automatic recognition of object classes from images (categorization). ...
Textons pairs which do not contribute to class discriminability are merged together by our learning algorithm (the pair ab in the figure). ...
doi:10.1109/iccv.2005.171
dblp:conf/iccv/WinnCM05
fatcat:v33h7mdf6zgypitt2jmz5dqaq4
Projective dictionary pair learning for pattern classification
2014
Neural Information Processing Systems
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We propose a new discriminative DL framework, namely projective dictionary pair learning (DPL), which learns a synthesis dictionary and an analysis dictionary jointly to achieve the goal of signal representation ...
Discriminative dictionary learning (DL) has been widely studied in various pattern classification problems. ...
First, we introduce a new DL framework, which extends the conventional discriminative synthesis dictionary learning to discriminative synthesis and analysis dictionary pair learning (DPL). ...
dblp:conf/nips/GuZZF14
fatcat:tfddi3x3ofds5hew2v5revghuy
Convolutional Dictionary Pair Learning Network for Image Representation Learning
[article]
2020
arXiv
pre-print
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learning schemes of the CNN and dictionary pair learning into a unified framework. ...
Generally, the architecture of CDPL-Net includes two convolutional/pooling layers and two dictionary pair learn-ing (DPL) layers in the representation learning module. ...
ACKNOWLEDGEMENTS This paper is partially supported by the National Natural Science Foundation of China (61672365, 61732008, 61725203, 61622305, 61871444 and 61806035) and the Fundamental Research Funds for ...
arXiv:1912.12138v3
fatcat:xqueqykknjfq7kemsk5eaweiiy
Color object recognition via cross-domain learning on RGB-D images
2016
2016 IEEE International Conference on Robotics and Automation (ICRA)
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We present a novel approach that utilizes labeled RGB-D data in the training stage, where depth features are extracted for enhancing the discriminative capability of the original learning system that only ...
In order to alleviate cross-domain discrepancy, we employ a state-of-the-art domain-adaptive dictionary learning algorithm that updates image representations in both domains and the classifier parameters ...
The cross-domain dictionary learning is then performed through learning a discriminative dictionary pair and the corresponding classifier simultaneously. by jointly learning the dictionary and the classification ...
doi:10.1109/icra.2016.7487308
dblp:conf/icra/HuangZSF16
fatcat:5g6qde5snbbbngbrbykoxt2vjq
Indoor Scene Recognition from RGB-D Images by Learning Scene Bases
2014
2014 22nd International Conference on Pattern Recognition
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Second, we learn class-specific sub-dictionaries that capture the high-order couplings between the objects and attributes. ...
In particular, elastic net regularization and geometric similarity constraint is imposed to increase the discriminative power of the sub-dictionaries. ...
Instead of learning one overcomplete dictionary for all classes, we learn class-specific sub-dictionaries to increase the discrimination. ...
doi:10.1109/icpr.2014.588
dblp:conf/icpr/WanHA14
fatcat:xz7zvhdbtrctxjpuhtw6ahuque
Robust Audio-Visual Instance Discrimination via Active Contrastive Set Mining
[article]
2022
arXiv
pre-print
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As a state-of-the-art solution, Audio-Visual Instance Discrimination (AVID) extends instance discrimination to the audio-visual realm. ...
The recent success of audio-visual representation learning can be largely attributed to their pervasive property of audio-visual synchronization, which can be used as self-annotated supervision. ...
Minimising NCE loss means maximising a lower bound on the MI between audio-visual pairs, which allows the model to learn to discriminate whether audio-visual pairs are "similar" or "dissimilar". ...
arXiv:2204.12366v1
fatcat:ixrdqdv4zjdknfpcyllv3yoqyq
Discriminative Dictionary Learning with Pairwise Constraints
[chapter]
2013
Lecture Notes in Computer Science
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We propose a unified discriminative dictionary learning approach for both pair matching and multiclass classification tasks. ...
The learned dictionary encourages feature points from a similar pair (or the same class) to have similar sparse codes. ...
As it shows, compared to KSVD, higher similarity scores for the 'same pairs' and lower similarity scores for 'different' pairs are obtained by our discriminative dictionary learning. ...
doi:10.1007/978-3-642-37331-2_25
fatcat:wt7sxuoozffa5fixnn73zdg2nq
Enhancing Action Recognition by Cross-Domain Dictionary Learning
2013
Procedings of the British Machine Vision Conference 2013
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through which a reconstructive, discriminative and domain-adaptive dictionary-pair can be learned. ...
We present a novel cross-dataset action recognition framework that utilizes relevant actions from other visual domains as auxiliary knowledge for enhancing the learning system in the target domain. ...
discriminative and domain-adaptive dictionary pair for data under different distributions. ...
doi:10.5244/c.27.52
dblp:conf/bmvc/ZhuS13
fatcat:p3cg4bbnfbdpro3bgkgpygfccu
DISCRIMINATIVE DICTIONARY PAIR LEARNING FOR IMAGE CLASSIFICATION
2020
Journal of Computer Science and Cybernetics
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In this paper, to improve the discriminability of the popular dictionary pair learning (DPL) algorithm, we propose a new method called discriminative dictionary pair learning (DDPL) for image classification ...
Dictionary learning (DL) for sparse coding has been widely applied in the field of computer vision. ...
[4] proposed discriminative dictionary pair learning method based on differentiable support vector function (DPL-SV) for visual recognition. Li et al. ...
doi:10.15625/1813-9663/36/4/15105
fatcat:2ucd3cubzbdt5ebmioobyjd7yi
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