Real-Time Object Segmentation using a Bag of Features Approach, 13th International Conference of the ACIA, L'Espluga de Francolí, 2010. ,
Efficient Object Pixel-Level Categorization Using Bag of Features, Advances in Visual Computing, pp.44-54, 2009. ,
DOI : 10.1007/978-3-642-10331-5_5
URL : http://www.iiia.csic.es/~mantaras/ISVC09.pdf
Fast and robust object segmentation with the Integral Linear Classifier, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.1046-1053, 2010. ,
DOI : 10.1109/CVPR.2010.5540098
URL : https://hal.archives-ouvertes.fr/inria-00548642
Proposal for a standard default color space for the internet -srgb, 4th Color and Imaging Conference, pp.238-245, 1996. ,
Pedestrian detection with a Large-Field-Of-View deep network, 2015 IEEE International Conference on Robotics and Automation (ICRA), 2015. ,
DOI : 10.1109/ICRA.2015.7139256
Real-Time Pedestrian Detection with Deep Network Cascades, Procedings of the British Machine Vision Conference 2015, 2015. ,
DOI : 10.5244/C.29.32
URL : http://www.bmva.org/bmvc/2015/papers/paper032/abstract032.pdf
All about VLAD, IEEE Conference on Computer Vision and Pattern Recognition, 2013. ,
Spatio-Temporal Convolutional Sparse Auto-Encoder for Sequence Classification, Procedings of the British Machine Vision Conference 2012, pp.124-125, 2012. ,
DOI : 10.5244/C.26.124
URL : https://hal.archives-ouvertes.fr/hal-01353046
Speeded-Up Robust Features (SURF), Computer Vision and Image Understanding, vol.110, issue.3, pp.346-359, 2008. ,
DOI : 10.1016/j.cviu.2007.09.014
URL : http://www.cs.jhu.edu/%7Emisha/ReadingSeminar/Papers/Bay08.pdf
Shape matching and object recognition using shape contexts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.4, pp.509-522, 2001. ,
DOI : 10.1109/34.993558
Pedestrian detection at 100 frames per second, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.2903-2910, 2012. ,
DOI : 10.1109/CVPR.2012.6248017
Ten Years of Pedestrian Detection, What Have We Learned?, pp.613-627, 2015. ,
DOI : 10.1007/978-3-319-16181-5_47
URL : http://arxiv.org/pdf/1411.4304.pdf
Online algorithms and stochastic approximations, Online Learning and Neural Networks, 1998. ,
Land use classification in remote sensing images by convolutional neural networks, 1508. ,
LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, pp.27-28, 2011. ,
DOI : 10.1145/1961189.1961199
Automatic localization of tombs in aerial imagery: Application to the digital archiving of cemetery heritage, 2013 Digital Heritage International Congress (DigitalHeritage), pp.657-660, 2013. ,
DOI : 10.1109/DigitalHeritage.2013.6743811
URL : https://hal.archives-ouvertes.fr/lirmm-01234256
Describing Textures in the Wild, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014. ,
DOI : 10.1109/CVPR.2014.461
URL : https://hal.archives-ouvertes.fr/hal-01109284
Image orientation detection using LBP-based features and logistic regression. Multimedia Tools and Applications, pp.3013-3034, 2015. ,
DOI : 10.1007/s11042-013-1766-4
Fast and accurate deep network learning by exponential linear units (elus), 2016. ,
Evaluation of Global Descriptors for Multimedia Retrieval in Medical Applications, 2010 Workshops on Database and Expert Systems Applications, pp.127-131, 2010. ,
DOI : 10.1109/DEXA.2010.44
Mean shift: a robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.5, pp.603-619, 2002. ,
DOI : 10.1109/34.1000236
URL : http://nichol.as/papers/Comaniciu/Mean%20Shift:%20A%20Robust%20Approach%20Toward.pdf
Segmentation automatique d'images numeriques : Application a la detection des tombes dans un cimetiere, 2012. ,
Summed-area tables for texture mapping, Proceedings of the 11th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH '84, pp.207-212, 1984. ,
DOI : 10.1145/800031.808600
R-FCN : object detection via region-based fully convolutional networks, 1605. ,
SIFT algorithm analysis and optimization, 2010 International Conference on Image Analysis and Signal Processing, pp.415-419, 2010. ,
DOI : 10.1109/IASP.2010.5476084
Histograms of Oriented Gradients for Human Detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.886-893, 2005. ,
DOI : 10.1109/CVPR.2005.177
URL : https://hal.archives-ouvertes.fr/inria-00548512
Face detection based on a new color space YCgCr, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), pp.909-921, 2003. ,
DOI : 10.1109/ICIP.2003.1247393
ImageNet: A large-scale hierarchical image database, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.248-255, 2009. ,
DOI : 10.1109/CVPR.2009.5206848
Rotation-invariant convolutional neural networks for galaxy morphology prediction. Monthly notives of the royal astronomical society, pp.1441-1459, 2015. ,
DOI : 10.1093/mnras/stv632
URL : http://arxiv.org/pdf/1503.07077
Pedestrian detection: A benchmark, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009. ,
DOI : 10.1109/CVPR.2009.5206631
Decaf : A deep convolutional activation feature for generic visual recognition, International Conference in Machine Learning (ICML), 2014. ,
Rotation-invariant local binary pattern texture classification, Electronics in Marine (ELMAR), 2012 Proceedings, pp.71-74, 2012. ,
Joint Optimization of Cascaded Classifiers for Computer Aided Detection, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007. ,
DOI : 10.1109/CVPR.2007.383093
URL : http://www.cs.rpi.edu/~bij2/doc/cvpr07_cascade.pdf
WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.4743-4752, 2016. ,
DOI : 10.1109/CVPR.2016.513
URL : https://hal.archives-ouvertes.fr/hal-01343785
The Pascal Visual Object Classes Challenge: A Retrospective, International Journal of Computer Vision, vol.34, issue.11, pp.98-136, 2015. ,
DOI : 10.1109/TPAMI.2012.204
LIBLI- NEAR : A library for large linear classification, Journal of Machine Learning Research, vol.9, pp.1871-1874, 2008. ,
Computer Vision : A Modern Approach, 2002. ,
URL : https://hal.archives-ouvertes.fr/hal-01063327
RETIN: A Content-Based Image Indexing and Retrieval System, Pattern Analysis & Applications, vol.4, issue.2-3, pp.153-173, 2001. ,
DOI : 10.1007/PL00014576
URL : http://www-etis.ensea.fr/~cord/perso/paa.ps.gz
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Journal of Computer and System Sciences, vol.55, issue.1, pp.119-139, 1997. ,
DOI : 10.1006/jcss.1997.1504
URL : https://doi.org/10.1006/jcss.1997.1504
A short introduction to boosting, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pp.1401-1406, 1999. ,
Converting Output Scores from Outlier Detection Algorithms into Probability Estimates, Sixth International Conference on Data Mining (ICDM'06), pp.212-221, 2006. ,
DOI : 10.1109/ICDM.2006.43
URL : http://www.cse.msu.edu/~ptan/papers/ICDM2.pdf
Convolutional face finder: a neural architecture for fast and robust face detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.11, pp.1408-1423, 2004. ,
DOI : 10.1109/TPAMI.2004.97
Color image processing : methods and applications : color feature detection : an overview, pp.203-226, 2006. ,
Understanding the difficulty of training deep feedforward neural networks, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS'10). Society for Artificial Intelligence and Statistics, 2010. ,
Textural Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics, vol.3, issue.6, pp.610-621, 1973. ,
DOI : 10.1109/TSMC.1973.4309314
URL : http://www.cis.rit.edu/~cnspci/references/dip/segmentation/haralick1973.pdf
Texture classification by center-symmetric auto-correlation, using Kullback discrimination of distributions, Pattern Recognition Letters, vol.16, issue.1, pp.1-10, 1995. ,
DOI : 10.1016/0167-8655(94)00061-7
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, 2015 IEEE International Conference on Computer Vision (ICCV), 2015. ,
DOI : 10.1109/ICCV.2015.123
URL : http://arxiv.org/pdf/1502.01852
Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. ,
DOI : 10.1109/CVPR.2016.90
URL : http://arxiv.org/pdf/1512.03385
Hierarchical classification and feature reduction for fast face detection with support vector machines, Pattern Recognition, vol.36, issue.9, pp.2007-2017, 2003. ,
DOI : 10.1016/S0031-3203(03)00062-1
URL : http://web.mit.edu/serre/www/publications/heisele_etal-HBPRCV05.pdf
Reducing the Dimensionality of Data with Neural Networks, Science, vol.313, issue.5786, pp.313504-507, 2006. ,
DOI : 10.1126/science.1127647
Determination of Color Space for Accurate Change Detection, 2006 International Conference on Image Processing, pp.3021-3024, 2006. ,
DOI : 10.1109/ICIP.2006.313003
Batch normalization : Accelerating deep network training by reducing internal covariate shift, International Conference on Machine Learning, pp.448-456, 2015. ,
What is the best multi-stage architecture for object recognition ? Aggregating local descriptors into a compact image representation, IEEE 12th International Conference on Computer Vision IEEE Conference on Computer Vision & Pattern Recognition, pp.2146-2153, 2009. ,
Measuring and Comparison of Edge Detectors in Color Spaces, International Journal of Control and Automation, vol.6, issue.5, pp.21-29, 2013. ,
DOI : 10.14257/ijca.2013.6.5.03
URL : https://doi.org/10.14257/ijca.2013.6.5.03
3D Convolutional Neural Networks for Human Action Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.1, pp.221-231, 2013. ,
DOI : 10.1109/TPAMI.2012.59
URL : http://www.dbs.informatik.uni-muenchen.de/%7Eyu_k/icml2010_3dcnn.pdf
Caffe, Proceedings of the ACM International Conference on Multimedia, MM '14, 2014. ,
DOI : 10.1145/2647868.2654889
Deep learning with sshaped rectified linear activation units, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp.1737-1743, 2016. ,
A Deep Learning Method Combined Sparse Autoencoder with SVM, 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, pp.257-260, 2015. ,
DOI : 10.1109/CyberC.2015.39
Texture-based foreground detection, Image Processing and Pattern Recognition, vol.4, issue.4, 2011. ,
Deep Neural Decision Forests, 2015 IEEE International Conference on Computer Vision (ICCV), 2016. ,
DOI : 10.1109/ICCV.2015.172
Learning multiple layers of features from tiny images, 2009. ,
ImageNet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems 25, pp.1097-1105, 2012. ,
DOI : 10.1162/neco.2009.10-08-881
URL : http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf
An Image Classification Algorithm Based on Bag of Visual Words and Multi-kernel Learning, Journal of Multimedia, vol.9, issue.2, p.2014 ,
DOI : 10.4304/jmm.9.2.269-277
Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, vol.60, issue.2, pp.91-110 ,
DOI : 10.1023/B:VISI.0000029664.99615.94
URL : http://www.cs.ubc.ca/~lowe/papers/ijcv03.ps
Feature ensemble learning based on sparse autoencoders for image classification, 2014 International Joint Conference on Neural Networks (IJCNN), pp.1739-1745, 2014. ,
DOI : 10.1109/IJCNN.2014.6889415
Nonlinear image representation using divisive normalization, Computer Vision and Pattern Recognition, 2008. ,
NeTra: A toolbox for navigating large image databases, Multimedia Systems, vol.7, issue.3, pp.184-198, 1999. ,
DOI : 10.1007/s005300050121
URL : http://www-iplab.ece.ucsb.edu/publications/99ACMNeTra.pdf
Comparison of pixelbased and object-oriented classification using ikonos imagery for automatic building extraction -safranbolu testfield, Fifth International Symposium Turkish-German Joint Geodetic Days, pp.28-31, 2006. ,
Subject independent facial expression recognition with robust face detection using a convolutional neural network, Neural Networks, vol.16, issue.5-6, pp.5-6555, 2003. ,
DOI : 10.1016/S0893-6080(03)00115-1
Optimizing cascade classifiers, 2005. ,
Neurocomputing : Foundations of research. chapter A Logical Calculus of the Ideas Immanent in Nervous Activity, pp.15-27, 1988. ,
Moment invariants for recognition under changing viewpoint and illumination, Computer Vision and Image Understanding, vol.94, issue.1-3 ,
DOI : 10.1016/j.cviu.2003.10.011
URL : https://lirias.kuleuven.be/bitstream/123456789/73521/1/Mindru_Tuytelaars_VanGool_Moons-moments-cviu.pdf
Randomized Clustering Forests for Image Classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.9, pp.1632-1646, 2008. ,
DOI : 10.1109/TPAMI.2007.70822
URL : https://hal.archives-ouvertes.fr/inria-00548666
Discriminative local binary patterns for human detection in personal album, Computer Vision and Pattern Recognition, pp.1-8, 2008. ,
Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm, 2010 Third International Symposium on Intelligent Information Technology and Security Informatics, pp.63-67, 2010. ,
DOI : 10.1109/IITSI.2010.74
Rectified linear units improve restricted boltzmann machines, Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp.807-814, 2010. ,
Scalable Recognition with a Vocabulary Tree, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06) ,
DOI : 10.1109/CVPR.2006.264
Implicit color segmentation features for pedestrian and object detection, 2009 IEEE 12th International Conference on Computer Vision, pp.723-730, 2009. ,
DOI : 10.1109/ICCV.2009.5459238
URL : http://www.comp.leeds.ac.uk/ott/dl/iccv2009.pdf
Trecvid 2015 ? an overview of the goals, tasks, data, evaluation mechanisms and metrics, Proceedings of TREC- VID 2015. NIST, USA, 2015. ,
Segmentation and edge detection of color images using CIELAB color space and edge detectors, INTERACT-2010, 2010. ,
DOI : 10.1109/INTERACT.2010.5706186
An efficient multiresolution SVM network approach for object detection in aerial images ,
URL : https://hal.archives-ouvertes.fr/lirmm-01234225
Speeding-up a convolutional neural network by connecting an SVM network, 2016 IEEE International Conference on Image Processing (ICIP), 2016. ,
DOI : 10.1109/ICIP.2016.7532766
URL : https://hal.archives-ouvertes.fr/hal-01374118
Detection of manhole covers in highresolution aerial images of urban areas by combining two methods, 2015 Joint Urban Remote Sensing Event (JURSE), pp.1-4, 2015. ,
URL : https://hal.archives-ouvertes.fr/lirmm-01275684
Detection of Manhole Covers in High-Resolution Aerial Images of Urban Areas by Combining Two Methods, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.9, issue.5, pp.1802-1807, 2016. ,
DOI : 10.1109/JSTARS.2015.2504401
URL : https://hal.archives-ouvertes.fr/lirmm-01275684
Optimizing color information processing inside an SVM network, IS&T International Symposium on Electronic Imaging Proceedings of Visual Information Processing and Communication VII, 2016. ,
DOI : 10.2352/ISSN.2470-1173.2016.2.VIPC-243
URL : https://hal.archives-ouvertes.fr/hal-01374090
Comparaison de la segmentation pixel et segmentation objet pour la détection d'objets multiples et variables dans des images, COmpression et REprésentation des Signaux Audiovisuels, 2014. ,
Etude des réseaux de neurones sur la stéganalyse, CORESA2016, COmpression et REprésentation des Signaux Audiovisuels, 2016. ,
Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover sourcemismatch, roceedings of Media Watermarking , Security, and Forensics, Part of IS&T International Symposium on Electronic Imaging, 2016. ,
DOI : 10.2352/ISSN.2470-1173.2016.8.MWSF-078
URL : https://hal.archives-ouvertes.fr/lirmm-01227950
Recurrent convolutional neural networks for scene labeling, Proceedings of the 31st International Conference on Machine Learning (ICML), 2014. ,
You Only Look Once: Unified, Real-Time Object Detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1506. ,
DOI : 10.1109/CVPR.2016.91
URL : http://arxiv.org/pdf/1506.02640
The perceptron ? A perceiving and recognizing automaton, 1957. ,
The perceptron: A probabilistic model for information storage and organization in the brain., Psychological Review, vol.65, issue.6, pp.65-386, 1958. ,
DOI : 10.1037/h0042519
Parallel distributed processing : Explorations in the microstructure of cognition, chapter Learning Internal Representations by Error Propagation, pp.318-362 ,
Evaluation of pooling operations in convolutional architectures for object recognition, Proceedings of the 20th International Conference on Artificial Neural Networks : Part III, ICANN'10, pp.92-101, 1986. ,
Beyond bags of features : Spatial pyramid matching for recognizing natural scene categories, CVPR, pp.2169-2178, 2006. ,
URL : https://hal.archives-ouvertes.fr/inria-00548585
Integrated recognition, localization and detection using convolutional networks. CoRR, abs/1312, 2013. ,
Semantic texton forests for image categorization and segmentation, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008. ,
DOI : 10.1109/CVPR.2008.4587503
Very deep convolutional networks for large-scale image recognition. CoRR, abs, 1409. ,
Video Google: a text retrieval approach to object matching in videos, Proceedings Ninth IEEE International Conference on Computer Vision, pp.1470-1477, 2003. ,
DOI : 10.1109/ICCV.2003.1238663
URL : http://www.cise.ufl.edu/class/cis6930fa07atc/Papers/Sivic03.pdf
Image Segmentation and Feature Extraction, IEEE Transactions on Systems, Man, and Cybernetics, vol.8, issue.4, pp.237-247, 1978. ,
DOI : 10.1109/TSMC.1978.4309944
Striving for simplicity : The all convolutional net, International Conference on Learning Representations, p.2015 ,
Going deeper with convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ,
DOI : 10.1109/CVPR.2015.7298594
URL : http://arxiv.org/pdf/1409.4842
Deep neural networks for object detection, Advances in Neural Information Processing Systems 26, pp.2553-2561, 2013. ,
Textural Features Corresponding to Visual Perception, IEEE Transactions on Systems, Man, and Cybernetics, vol.8, issue.6, pp.460-473, 1978. ,
DOI : 10.1109/TSMC.1978.4309999
Object detection based on convolutional neural network, Report, 2015. ,
Segmentation automatique d'images numeriques : Application a la detection des tombes dans un cimetiere, 2013. ,
Evaluating Color Descriptors for Object and Scene Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.9, pp.1582-1596, 2010. ,
DOI : 10.1109/TPAMI.2009.154
Fisher and VLAD with FLAIR, IEEE Conference on Computer Vision and Pattern Recognition, 2014. ,
Visualizing high-dimensional data using t-SNE, Journal of Machine Learning Research, vol.9, pp.2579-2605, 2008. ,
Rapid object detection using a boosted cascade of simple features, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, pp.511-518, 2001. ,
DOI : 10.1109/CVPR.2001.990517
URL : http://www.cc.gatech.edu/ccg/./paper_of_week/viola01rapid.pdf
Locality-constrained Linear Coding for image classification, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.3360-3367, 2010. ,
DOI : 10.1109/CVPR.2010.5540018
URL : http://www.ifp.illinois.edu/%7Ejyang29/papers/CVPR10-LLC.pdf
An HOG-LBP human detector with partial occlusion handling, 2009 IEEE 12th International Conference on Computer Vision, pp.32-39, 2009. ,
DOI : 10.1109/ICCV.2009.5459207
Deep image : Scaling up image recognition, 1501. ,
Probability estimates for multi-class classification by pairwise coupling, J. Mach. Learn. Res, vol.5, pp.975-1005, 2004. ,
Deep learning of feature representation with multiple instance learning for medical image analysis, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.1626-1630, 2014. ,
DOI : 10.1109/ICASSP.2014.6853873
Orientation robust object detection in aerial images using deep convolutional neural network, 2015 IEEE International Conference on Image Processing (ICIP), pp.3735-3739, 2015. ,
DOI : 10.1109/ICIP.2015.7351502
Fast human detection using a cascade of histograms of oriented gradients, CVPR06, pp.1491-1498, 2006. ,