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Manifold-based Test Generation for Image Classifiers [article]

Taejoon Byun, Abhishek Vijayakumar, Sanjai Rayadurgam, Darren Cofer
2020 arXiv   pre-print
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]

Blerta Lindqvist, Rauf Izmailov
2020 arXiv   pre-print
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

Kui-yu Chang, Joydeep Ghosh, Nasser M. Nasrabadi, Aggelos K. Katsaggelos
2000 Applications of Artificial Neural Networks in Image Processing V  
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]

Jayaraman J. Thiagarajan, Vivek Narayanaswamy, Deepta Rajan, Jason Liang, Akshay Chaudhari, Andreas Spanias
2021 arXiv   pre-print
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]

Guanxiong Liu, Issa Khalil, Abdallah Khreishah, Abdulelah Algosaibi, Adel Aldalbahi, Mohammed Alaneem, Abdulaziz Alhumam, Mohammed Anan
2020 arXiv   pre-print
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]

Manisha Kalra, P. Deepti, R. Abhilash, Sukhendu Das
2006 Lecture Notes in Computer Science  
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

Mohamed Farouk, Alistair Sutherland
2018 ICT Express  
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]

Seiji Hotta
2008 Pattern Recognition Techniques, Technology and Applications  
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:  ... 
doi:10.5772/6247 fatcat:cqjzuf35tzfhpmckazxnskhjce

ManiGen: A Manifold Aided Black-box Generator of Adversarial Examples

Guanxiong Liu, Issa Khalil, Abdallah Khreishah, Abdulelah Algosaibi, Adel Aldalbahi, Mohammed Alaneem, Abdulaziz Alhumam, Mohammed Anan
2020 IEEE Access  
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

A. Shabbir, G. Verdoolaege
2015 2015 IEEE International Conference on Image Processing (ICIP)  
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

Seiji Hotta
2009 IAPR International Workshop on Machine Vision Applications  
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

Raviteja Vemulapalli, Jaishanker K. Pillai, Rama Chellappa
2013 2013 IEEE Conference on Computer Vision and Pattern Recognition  
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  
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

Wonkook Kim, Yangchi Chen, Melba M. Crawford, James C. Tilton, Joydeep Ghosh
2007 2007 IEEE International Geoscience and Remote Sensing Symposium  
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

Dong Hye Ye, Benoit Desjardins, Jihun Hamm, Harold Litt, Kilian M. Pohl
2014 IEEE Transactions on Medical Imaging  
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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