A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Filters
Sparse approximated nearest points for image set classification
2011
CVPR 2011
Preserved Fulltext
the nearest points as well as their sparse approximations. ...
We introduce a novel between-set distance called Sparse Approximated Nearest Point (SANP) distance. ...
Cevikalp for sharing the source code of AHISD/CHISD and providing the LBP features for the Mobo dataset. ...
doi:10.1109/cvpr.2011.5995500
dblp:conf/cvpr/HuMO11
fatcat:dot53wdgnjecfm4oadagki2iqq
Improved Image Set Classification via Joint Sparse Approximated Nearest Subspaces
2013
2013 IEEE Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Sparse Approximated Nearest Points (SANP) and Manifold Discriminant Analysis (MDA). ...
Existing multi-model approaches for image set classification extract local models by clustering each image set individually only once, with fixed clusters used for matching with other image sets. ...
Thus the image set classification problem can be transferred to a point classification problem on Grassmann manifolds. ...
doi:10.1109/cvpr.2013.65
dblp:conf/cvpr/ChenSHL13
fatcat:tkjau746sjfxze3g2tpuasukwq
Face Recognition Using Sparse Approximated Nearest Points between Image Sets
2012
IEEE Transactions on Pattern Analysis and Machine Intelligence
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2014; you can also visit the original URL.
The file type is application/pdf.
SANPs are the nearest points of two image sets such that each point can be sparsely approximated by the image samples of its respective set. ...
The model accounts for unseen appearances in the form of affine combinations of sample images. To calculate the between-set distance, we introduce the Sparse Approximated Nearest Point (SANP). ...
Cevikalp for sharing the source code of AHISD/CHISD and providing the LBP features for the Mobo dataset. ...
doi:10.1109/tpami.2011.283
pmid:22213765
fatcat:goxrlpqxwnhzpjb2d5rhlj6jwq
Face recognition based on regularized nearest points between image sets
2013
2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
In this paper, a novel regularized nea method is proposed for image sets based face modeling an image set as a regularized affine regularized nearest points (RNP), one on each are automatically determined ...
by an efficient ite between-set distance of RNP is then defined by the distance between the RNPs and the structu Compared with the recently developed spar nearest points (SANP) method, RNP has formulation ...
By modeling each image set as an affine hull, Hu et al. selected two points (one point in each image set) with the closest distance as the sparse approximated nearest points (SANP), where SANPs are required ...
doi:10.1109/fg.2013.6553727
dblp:conf/fgr/YangZGZ13
fatcat:rjwqmebk6rc3jglz2jbeitxs2q
Learning dictionary on manifolds for image classification
2013
Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Sparse representation algorithm combined with k-nearest neighbors is instead utilized to construct the topological structures, because it is capable of approximating the data point by selecting its homogenous ...
The image features are approximated as a linear combination of bases selected from the dictionary in a sparse space, resulting in compact patterns. ...
We would like to thank Qianhaoze You for the suggestion on revision of this manuscript. ...
doi:10.1016/j.patcog.2012.11.018
fatcat:btrvnw3u7zaihcnojrnev3gurm
Prototype Discriminative Learning for Face Image Set Classification
[chapter]
2017
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
This paper presents a novel Prototype Discriminative Learning (PDL) method to solve the problem of face image set classification. ...
We aim to simultaneously learn a set of prototypes for each image set and a linear discriminative transformation to make projections on the target subspace satisfy that each image set can be optimally ...
[1] , Sparse Approximated Nearest Point (SANP) [2] , Regularized Nearest Points (RNP) [3] , Dual Linear Regression Classification (DLRC) [28] and Set-to-Set Distance Metric Learning (SSDML) [5] . ...
doi:10.1007/978-3-319-54187-7_23
fatcat:qnfhtjqxw5cyviss6zg3qvhpx4
Representation Learning with Deep Extreme Learning Machines for Efficient Image Set Classification
[article]
2015
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Efficient and accurate joint representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification. ...
Extensive experiments on a broad range of public datasets for image set classification (Honda/UCSD, CMU Mobo, YouTube Celebrities, Celebrity-1000, ETH-80) show that the proposed algorithm consistently ...
[7] approximate each of the two nearest points between two image sets by a sparse combination of the corresponding set samples. ...
arXiv:1503.02445v3
fatcat:2x3pddwt3jagldi2asogmgnvlq
Asymmetric Sparse Kernel Approximations for Large-Scale Visual Search
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
We introduce an asymmetric sparse approximate embedding optimized for fast kernel comparison operations arising in large-scale visual search. ...
We evaluate our method on three benchmark image retrieval datasets: SIFT1M, ImageNet, and 80M-TinyImages. ...
Sparse kernel approximations were recently used to provide significant speed up in image-based classification and detection tasks in [23] , where it is shown that PQ can be cast as a sparse kernel approximation ...
doi:10.1109/cvpr.2014.271
dblp:conf/cvpr/DavisBS14
fatcat:6q4nqsuvyrbc3hnfoeu6ci3ijm
Graph Regularized Sparse Coding for Image Representation
2011
IEEE Transactions on Image Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
The extensive experimental results on image classification and clustering have demonstrated the effectiveness of our proposed algorithm. ...
It is an unsupervised learning algorithm, which finds a basis set capturing high-level semantics in the data and learns sparse coordinates in terms of the basis set. ...
Image Classification For image classification, we present experiments on the benchmark USPS handwritten digits data set. 1 USPS is composed of 7291 training images and 2007 test images of size 16 16 . ...
doi:10.1109/tip.2010.2090535
pmid:21047712
fatcat:6rfbyic7kzf55nra5sdoydbrda
Bayesian Markov Chain Random Field Cosimulation for Improving Land Cover Classification Accuracy
2014
Mathematical Geosciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf.
It was tested using a series of expert-interpreted data sets and an image data set pre-classified by the supervised maximum likelihood (SML) algorithm. Results show that with the density ...
This study introduces a Bayesian Markov chain random field (MCRF) cosimulation approach for improving land-use/land-cover (LULC) classification accuracy through integrating expert-interpreted data and ...
From the dense data set, a medium data set (406 data points), a sparse data set (203 data point, Fig. 5d ), an extra-sparse data set (100 data points) and an extreme-sparse data set (50 data points) were ...
doi:10.1007/s11004-014-9553-y
fatcat:6kgdnqny4jep3jezrvbuc457cm
Sparse Projections of Medical Images onto Manifolds
[article]
2013
arXiv
pre-print
Preserved Fulltext
The Internet Archive has a preservation copy of this work in our general collections.
The file type is application/pdf.
Other Versions
Our method involves solving a simple convex optimization problem and has the attractive property of guaranteeing an upper bound on the approximation error, which is crucial for medical applications. ...
We interpret our method as an out-of-sample extension that approximates kernel ridge regression. ...
We thank Siemens Healthcare for image data. ...
arXiv:1303.5508v2
fatcat:xuefvzjegfgxbfo77fj3326kkq
Saliency Aware Locality-preserving Coding for Image Classification
2012
2012 IEEE International Conference on Multimedia and Expo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
The Bag-of-Features (BOF) model is widely used for image classification. ...
However, recent locality-preserving coding schemes do not account for the saliency characteristic during the process of generating the raw image representations. ...
Max-pooling and 2 normalization are adopted to generate the final image representation. Lib-linear SVM [13] is used for classification wherein the penalty coefficient is set to 10. ...
doi:10.1109/icme.2012.164
dblp:conf/icmcs/FangSX12
fatcat:ajfob5e5yrhf7k7ckr2padhjjq
A Fast and Robust Sparse Approach for Hyperspectral Data Classification Using a Few Labeled Samples
2012
IEEE Transactions on Geoscience and Remote Sensing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Extensive experiments on four real hyperspectral data sets prove that hyperspectral data is highly sparse in nature, and the proposed approaches are robust across different databases, offer more classification ...
The proposed approaches are tested for our difficult classification problem of hyperspectral data with few labeled samples. ...
ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. ...
doi:10.1109/tgrs.2011.2172617
fatcat:sg6xrpsxujfexlmumovqgtusp4
Palmprint Recognition Based on Image Sets
[chapter]
2015
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
After the feature extraction process, we use the method of sparse approximated nearest points (SANP) for palmprint image set classification. ...
Consequently, in this paper, we present a novel approach for palmprint recognition based on image sets. ...
There for a new term: Sparse Approximated Nearest Points (SANP) is put forward to calculate the distance between different image sets. ...
doi:10.1007/978-3-319-22180-9_30
fatcat:foz4dt4rzje4zkhci5zenrfv24
An enhanced sparse representation strategy for signal classification
2012
Compressive Sensing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf.
Sparse representation based classification (SRC) has achieved state-of-the-art results on face recognition. ...
This assumption is reasonable for face recognition as images of the same subject under different intensity levels are still considered to be of same-class. ...
For example, sparse representation-based classification (SRC) 4 has obtained impressive results on face recognition, which encodes a query face image over the entire set of training template images and ...
doi:10.1117/12.919469
fatcat:3p47xyzj7nggxb322lifsikbmq
« Previous
Showing results 1 — 15 out of 31,419 results