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On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
2015
PLoS ONE
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This work proposes a general solution to the problem of understanding classification decisions by pixel-wise decomposition of nonlinear classifiers. ...
We introduce a methodology that allows to visualize the contributions of single pixels to predictions for kernel-based classifiers over Bag of Words features and for multilayered neural networks. ...
KRM thanks for partial funding by the National Research Foundation of Korea funded by the Ministry of Education, Science, and Technology in the BK21 program. ...
doi:10.1371/journal.pone.0130140
pmid:26161953
pmcid:PMC4498753
fatcat:gfb4q6cdirggbd7auywpploz6m
Tumor Diagnosis against Other Brain Diseases Using T2 MRI Brain Images and CNN Binary Classifier and DWT
2023
Brain Sciences
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Firstly, a compression step is applied for each MRI scan applying DWT up to three levels of decomposition. ...
The results are promising for the proposed CNN based on DWT knowledge to serve for binary diagnosis of glioma tumors among other tumors and diseases. ...
In the MRI image, the non-healthy area is segmented where pixel intensity is higher or lower both row-wise and column-wise in the image matrix. ...
doi:10.3390/brainsci13020348
pmid:36831891
pmcid:PMC9954603
fatcat:4yaouqtna5acxmimk4jhezradi
Fiber orientation assessment on randomly-oriented strand composites by means of infrared thermography
2015
Composites Science And Technology
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The classification rate obtained with the network was 91.2% for the training stage and 71.6% for the testing stage. ...
Artificial neural network (ANN) is then used to estimate the fiber orientation over the heated line. ...
Then, the pixels of each sample (from the EOF image) are rearranged line-wise. ...
doi:10.1016/j.compscitech.2015.10.015
fatcat:xutbkvs3d5dgxkjjbjrxwgpqme
Structured Binary Neural Networks for Image Recognition
[article]
2022
arXiv
pre-print
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Furthermore, for the first time, we apply binary neural networks to object detection. ...
In particular, we propose a "network decomposition" strategy, termed Group-Net, in which we divide the network into groups. ...
In particular, we consider semantic segmentation which can be deemed as a dense pixel-wise classification problem. ...
arXiv:1909.09934v4
fatcat:j7nx2mkcrbbkdobfzwg5l7elwy
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure
[article]
2022
arXiv
pre-print
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Finally, we extend the traditional attribution methods by proposing a new kernel enabling an ANOVA-like orthogonal decomposition of importance scores based on HSIC, allowing us to evaluate not only the ...
Indeed, we improve or match the state-of-the-art of both black-box and white-box attribution methods for several fidelity metrics on Imagenet with various recent model architectures. ...
Acknowledgements This work has benefited from the AI Interdisciplinary Institute ANITI, which is funded by the French "Investing for the Future -PIA3" program under the Grant agreement ANR-19-P3IA-0004 ...
arXiv:2206.06219v3
fatcat:vvooxmrywjak5iuk3wk46fep5q
Hashed Binary Search Sampling for Convolutional Network Training with Large Overhead Image Patches
[article]
2017
arXiv
pre-print
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A novel binary search tree sampling scheme is fused with a kernel based hashing procedure that maps image patches into hash-buckets using binary codes generated from image content. ...
However, random sampling selection criteria often leads to redundant and noisy-image patches for model training. ...
The authors would like to thank Jeanette Weaver for her contribution on selecting testing sites and preparing testing images. ...
arXiv:1707.05685v1
fatcat:a2264v5cpjaptdktpomgqhteom
Table of contents
2020
IEEE Transactions on Geoscience and Remote Sensing
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Chanussot 6420 CSVM Architectures for Pixel-Wise Object Detection in High-Resolution Remote Sensing Images ..................... ........................................................................ ...
Cui 6020 A Patch-to-Pixel Convolutional Neural Network for Small Ship Detection With PolSAR Images ....................... ......................................................................... K. ...
doi:10.1109/tgrs.2020.3006605
fatcat:g45mqghjmjenlfd2ydw7nzbcju
A NOVELTY APPROACH OF SPATIAL CO-OCCURRENCE AND DISCRETE SHEARLET TRANSFORM BASED TEXTURE CLASSIFICATION USING LPBOOSTING CLASSIFIER
2014
Journal of Computer Science
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Recently, the research towards Brodatz database for texture classification done at considerable amount of study has been published, the effective classification are vulnerable towards for training and ...
Hence, the proposed method for the feature extraction and classification of texture suggested with the experimentation through the spatial co-occurrence matrix with the power spectrum based discrete shearlet ...
For 2level decomposition, the classification accuracy is 99.776% (32 directions). The maximum classification accuracy achieved by 3-level decomposition with 64 directions is 99.866%. ...
doi:10.3844/jcssp.2014.783.793
fatcat:jsvmvu2lfzcl5hen3w3eyxkrm4
On the Preservation of Spatio-temporal Information in Machine Learning Applications
[article]
2020
arXiv
pre-print
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A generalization of shift-invariant k-means, convolutional dictionary learning, is then utilized as an unsupervised feature extraction method for classification. ...
In conventional machine learning applications, each data attribute is assumed to be orthogonal to others. ...
This unsupervised convolutional decomposition of a signal can be regarded as a feature extraction method that tackles the problem of orthogonality, where the extracted features for the i th data point ...
arXiv:2006.08321v1
fatcat:fokwd5vtvrdipkyc2tmavaivju
Identifying Informal Settlements Using Contourlet Assisted Deep Learning
2020
Sensors
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This work also analyzes the effects of wavelet and contourlet decompositions in the U-net architecture. ...
It was found that the proposed method has better class-discriminating power as compared to existing methods and has an overall classification accuracy of 94.9–95.7%. ...
Acknowledgments: The authors would like to thank all the anonymous reviewers and the editor for their valuable input and comments.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s20092733
pmid:32403308
pmcid:PMC7248841
fatcat:ct6ufzgwqna33c3iqimfzba3om
Local Binary Patterns and Its Application to Facial Analysis
2022
Zenodo
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This paper facial analysis process and different local binary pattern techniques applied for facial detection and recognition are extensively reviewed. ...
Keywords: Local Binary Pattern, LBP, Face Detection, Recognition. ...
Research Publish Journals
Orthogonal difference LBP for face analysis This research introduces the novel descriptor for Face analysis called as orthogonal difference-Local binary pattern (OD-LBP). ...
doi:10.5281/zenodo.7014366
fatcat:e2fsmogfxbdwnfehwnrqudyilq
Towards a computer-aided diagnosis system for pigmented skin lesions
2003
Computerized Medical Imaging and Graphics
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We demonstrate that our scheme outperforms methods based on the principal component decomposition, which is widely used for this kind of application. q ...
This paper presents a computer-aided diagnosis system for pigmented skin lesions, with solutions for the lesion boundary detection and for the quantification of the degree of symmetry. ...
Murat Kunt for encouraging and supporting this project. This research was funded by the Swiss National Science Foundation, fund no. 3252-053175. ...
doi:10.1016/s0895-6111(02)00048-4
pmid:12573891
fatcat:fctjd4wvmzenhao65w33yhu3pm
Hierarchical Matching Pursuit for Image Classification: Architecture and Fast Algorithms
2011
Neural Information Processing Systems
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Extracting good representations from images is essential for many computer vision tasks. ...
We investigate the architecture of HMP, and show that all three components are critical for good performance. ...
For computational efficiency, we perform our spatial pyramid max pooling across the image with a step size of 4 pixels, rather than at each pixel. ...
dblp:conf/nips/BoRF11
fatcat:y5brcvus75bqzbrgeinsef5ez4
Automatic Land Cover Reconstruction From Historical Aerial Images: An Evaluation of Features Extraction and Classification Algorithms
2019
IEEE Transactions on Image Processing
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Secondly, an extensive comparison study of state-of-the-art texture features extraction and classification algorithms including deep convolutional neural networks (DCNNs) has been performed. ...
Thirdly, a novel low-dimensional local texture filter named Rotated-CorneR Local Binary Pattern (R-CRLBP) is presented as a simplification of the Binary Gradient Contours filter through the use of an orthogonal ...
Researchers have been able to demonstrate successful segmentation and classification results at both coarse and fine grained scales (e.g. individual buildings [4] [16] , pixel [13] ) by wisely combining ...
doi:10.1109/tip.2019.2896492
fatcat:7nky7sqsp5eoppoep6wu7a6zsm
Change Detection in SAR Images Based on Deep Semi-NMF and SVD Networks
2017
Remote Sensing
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Finally, pixels in both multi-temporal SAR images are classified by the SVD networks, and then the final change map can be obtained. ...
The proposed method uses two singular value decomposition (SVD) analyses to learn the non-linear relations between multi-temporal images. ...
Acknowledgments: The authors would like to thank the editors and anonymous reviewers for their valuable comments and helpful suggestions, which greatly improved the quality of the paper. ...
doi:10.3390/rs9050435
fatcat:xobmqubtqrhq3mye74tnfthqw4
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