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Manifold-based Test Generation for Image Classifiers
[article]
2020
arXiv
pre-print
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Neural networks used for image classification tasks in critical applications must be tested with sufficient realistic data to assure their correctness. ...
A variant of Conditional Variational Autoencoder (CVAE) is used for capturing this manifold with a generative function, and a search technique is applied on this manifold space to efficiently find fault-revealing ...
MANIFOLD-BASED TEST GENERATION The goal of testing an image classifier is to find faults in a model which cause discordances between existing conditions and required conditions [1] . ...
arXiv:2002.06337v1
fatcat:l7bujunb7vclphnxg4rvdup2hi
Minimax Defense against Gradient-based Adversarial Attacks
[article]
2020
arXiv
pre-print
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The gradient of a classifier's loss function is used by gradient-based adversarial attacks to generate adversarially perturbed images. ...
Our Minimax adversarial approach presents a significant shift in defense strategy for neural network classifiers. ...
For CIFAR-10, we use RMSprop for the discriminator and classifier; Adadelta for the generator; and SGD for the adversarial model. ...
arXiv:2002.01256v1
fatcat:ivus7rlt4na3nb6rvrxth5mvle
Three-dimensional model-based object recognition and pose estimation using probabilistic principal surfaces
2000
Applications of Artificial Neural Networks in Image Processing V
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A novel scheme using spherical manifolds is proposed for the simultaneous classification and pose estimation of 3-D objects from 2-D images. ...
The spherical manifold imposes a local topological constraint on samples that are close to each other, while maintaining a global structure. ...
, (d) 3-D rendered image based on classifier output. ...
doi:10.1117/12.382913
fatcat:tmeengnbfndwfbdxctaa6exv54
Designing Counterfactual Generators using Deep Model Inversion
[article]
2021
arXiv
pre-print
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for a given query image. ...
We propose DISC (Deep Inversion for Synthesizing Counterfactuals) that improves upon deep inversion by utilizing (a) stronger image priors, (b) incorporating a novel manifold consistency objective and ...
There are four key components that are critical to designing classifier-based counterfactual generators: (i) choice of metric for semantics preservation; (ii) choice of image priors to regularize image ...
arXiv:2109.14274v2
fatcat:gdcwrxjg4vgtdic4ai5jjkcnxi
ManiGen: A Manifold Aided Black-box Generator of Adversarial Examples
[article]
2020
arXiv
pre-print
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However, it has been shown that neural network based classifiers are vulnerable to adversarial examples. ...
Most of the existing methods for generating such perturbations require a certain level of knowledge about the target classifier, which makes them not very practical. ...
MANIFOLD BASED ATTACK MODEL In this section, we introduce our approach for generating adversarial examples. ...
arXiv:2007.05817v1
fatcat:3g5df7yp6be3dlt5zg4deldpom
Pose Invariant Generic Object Recognition with Orthogonal Axis Manifolds in Linear Subspace
[chapter]
2006
Lecture Notes in Computer Science
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For matching based on shape features, we propose the use of distance transform based correlation. ...
Experiments were conducted on COIL-100 and IGOIL (IITM Generic Object Image Library) databases which contain objects with complex appearance and shape characteristics. ...
Fig. 1 . 1 Flowchart for Generic Object Recognition framework combining appearance (Linear Subspace Analysis) and Shape (DT based matching) cues
Fig. 2 . 2 Expected manifold set for an object in the ...
doi:10.1007/11949619_55
fatcat:re2k2b2ww5eotkaqe7xk6jtdfy
Principal component pyramids for manifold learning in hand shape recognition
2018
ICT Express
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Then shape manifolds are used for classifying the shape at the second stage. Nonlinearity reduction in manifolds using image blurring has been introduced in this thesis and tested on our set of example ...
The proposed algorithms are based on using image blurring to reduce the nonlinearity in the manifolds generated by applying PCA to a set of example images using a computer-generated model. ...
doi:10.1016/j.icte.2018.04.009
fatcat:ldjt75n3jvevjotzu5dowcklou
Manifold Matching for High-Dimensional Pattern Recognition
[chapter]
2008
Pattern Recognition Techniques, Technology and Applications
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Manifold matching In general, linear manifold-based classifiers are derived with principal component analysis (PCA). ...
Generally, orthonormal bases obtained with principal component analysis (PCA) are used for forming linear manifolds, but there is no guarantee that they are the best ones for achieving high accuracy. ...
Manifold Matching for High-Dimensional Pattern Recognition, Pattern Recognition Techniques, Technology and Applications, Peng-Yeng Yin (Ed.), ISBN: 978-953-7619-24-4, InTech, Available from: http://www.intechopen.com ...
doi:10.5772/6247
fatcat:cqjzuf35tzfhpmckazxnskhjce
ManiGen: A Manifold Aided Black-box Generator of Adversarial Examples
2020
IEEE Access
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ACKNOWLEDGEMENT The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number 1120. ...
MANIFOLD BASED ATTACK MODEL Based on the general format introduced in Section II, we show our approximation of the optimization problem. ...
For the STL10 dataset classifier, we apply transfer learning based on VGG16 model [22] trained on ImageNet dataset [23] . ...
doi:10.1109/access.2020.3029270
fatcat:ryr54jux25blxmb6gyrmrvdoem
Multivariate texture discrimination using a principal geodesic classifier
2015
2015 IEEE International Conference on Image Processing (ICIP)
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We use the Rao geodesic distance (GD) for calculating distances on the manifold. ...
The method, which we call principal geodesic classification, then determines the shortest distance from a test texture to the principal geodesic of each class. ...
In the training phase of the principal geodesic classifier, the principal geodesic for each class is computed assuming that the label for each texture image is known. 640 subimages are each used as a test ...
doi:10.1109/icip.2015.7351465
dblp:conf/icip/ShabbirV15
fatcat:7ull6tjmlzhz7doazhxzebwgxu
Color Image Classification Using Locally Linear Manifolds and Learning
2009
IAPR International Workshop on Machine Vision Applications
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Our method classifies a test image into the class that has the shortest total sum of the projection distances between test blocks and their corresponding linear manifolds. ...
In this paper, we propose block matching and learning using linear manifolds (affine subspaces) for color image classification. In our method, training images are partitioned into small size blocks. ...
Hence, we apply a learning rule based on generalized learning vector quantization (GLVQ) [7] to locally linear manifolds for improving accuracy. ...
dblp:conf/mva/Hotta09
fatcat:gi35bobcgfctnccaxbt7f2xnoa
Kernel Learning for Extrinsic Classification of Manifold Features
2013
2013 IEEE Conference on Computer Vision and Pattern Recognition
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Experimental results on image set-based classification and activity recognition clearly demonstrate the superiority of the proposed approach over existing methods for classification of manifold features ...
However, for kernel based approaches, poor choice of kernel often results in reduced performance. ...
Contributions: 1) We introduce a general framework for developing extrinsic classifiers for features that lie on Riemannian manifolds using the kernel learning approach. ...
doi:10.1109/cvpr.2013.233
dblp:conf/cvpr/VemulapalliPC13
fatcat:fwfmnkoeozdtdp3eyyisrivdje
Local Subspace Classifier with Transform-Invariance for Image Classification
2008
IEICE transactions on information and systems
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Based on these properties, LSC can outperform the kNN rule and conventional subspace classifiers in image classification. ...
The KLSC method is a very general classifier, but we must select an appropriate kernel function for high accuracy. ...
doi:10.1093/ietisy/e91-d.6.1756
fatcat:t6toltpvcfgv5dnqmpesf3oa6m
Multiresolution manifold learning for classification of hyperspectral data
2007
2007 IEEE International Geoscience and Remote Sensing Symposium
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As a framework for integrating spatial and spectral information associated with image samples, a hierarchical spatial-spectral segmentation method is investigated for constructing the manifold structure ...
Classification accuracies and generalization capability are compared to those achieved by the best basis binary hierarchical classifier, the hierarchical support vector machine classifier, and the shortest ...
We thank Amy Neuenschwander of the UT Center for Space Research for help in pre-processing the Hyperion data and interpreting the overall classification results. ...
doi:10.1109/igarss.2007.4423667
dblp:conf/igarss/KimCCTG07
fatcat:x6x432jwandk5im75m7pu7iqca
Regional Manifold Learning for Disease Classification
2014
IEEE Transactions on Medical Imaging
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To address this issue, we parcellate images into regions and then separately learn the manifold for each region. ...
We use the regional manifolds as low-dimensional descriptors of high-dimensional morphological image features, which are then fed into a classifier to identify regions affected by disease. ...
In addition, the resulting manifold is generally an inaccurate approximation of the true : An overview of the regional manifold-based classifier. ...
doi:10.1109/tmi.2014.2305751
pmid:24893254
pmcid:PMC5450500
fatcat:qd5l7lbnbnc7jbkughbvu3a6fy
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