1. .. Présentation-de-la-base-romane, , p.90

1. .. Résultats-obtenus-sur-romane, , p.94

C. .. Résultats-obtenus-sur,

.. .. Résultats,

. .. Bilan,

. , 1 Interaction pour la recherche d'images par similarité

. , Approche interactive basée sur l'adaptation du gain d'information

. .. , , p.115

. , 117 5.2.3 Stratégie de récupération des retours utilisateurs

. .. Expérimentations,

A. Annexe, Quelques chiffres

G. Alain and Y. Bengio, What regularized auto-encoders learn from the data-generating distribution, The Journal of Machine Learning Research, vol.15, issue.1, pp.3563-3593, 2014.

P. Awasthi, M. F. Balcan, and K. Voevodski, Local algorithms for interactive clustering, Journal of Machine Learning Research, vol.18, issue.3, pp.1-35, 2017.

A. E. Abdel-hakim, A. A. Farag, ;. Angelova, A. Krizhevsky, V. Vanhoucke et al., Csift : A sift descriptor with color invariant characteristics, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), vol.2, p.4, 1978.

. Amsterdam, Amsterdam city data

D. Angluin, Queries and concept learning, Machine learning, vol.2, issue.4, pp.319-342, 1988.

A. Alahi, R. Ortiz, and P. Vandergheynst, Freak : Fast retina keypoint, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.510-517, 2012.

G. Amati and C. J. Van-rijsbergen, Probabilistic models of information retrieval based on measuring the divergence from randomness, ACM Trans. Inf. Syst, vol.20, issue.4, pp.357-389, 2002.

Y. Bengio, R. Ducharme, P. Vincent, and C. Jauvin, A neural probabilistic language model, Journal of machine learning research, vol.3, pp.1137-1155, 2003.

P. R. Beaudet, Rotationally invariant image operators, Proceedings of the 4th International Joint Conference on Pattern Recognition, pp.579-583, 1978.

A. Borji and L. Itti, State-of-the-art in visual attention modeling, IEEE Trans. Pattern Anal. Mach. Intell, vol.35, issue.1, pp.185-207, 2013.

S. Bansal and E. R. Kaur, A review on content based image retrieval using svm, 2014.

;. Bibliographie, . Boureau, and . Le-cun, Sparse feature learning for deep belief networks, Advances in neural information processing systems, pp.1185-1192, 2008.

A. Babenko, A. Slesarev, A. Chigorin, and V. Lempitsky, Neural codes for image retrieval, 2014.

H. Bay, T. Tuytelaars, and L. Van-gool, Surf : Speeded up robust features, Computer Vision-ECCV 2006, pp.404-417, 2006.

A. Bosch, A. Zisserman, and X. Muñoz, Scene classification using a hybrid generative/discriminative approach, IEEE Trans. Pattern Anal. Mach. Intell, vol.30, issue.4, pp.712-727, 2008.

M. Cornia, L. Baraldi, G. Serra, and R. Cucchiara, Multi-level net : a visual saliency prediction model, European Conference on Computer Vision, pp.302-315, 2016.

M. Cornia, L. Baraldi, G. Serra, and R. Cucchiara, Predicting human eye fixations via an lstm-based saliency attentive model, 2016.

G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray, Visual categorization with bags of keypoints, Workshop on Statistical Learning in Computer Vision, ECCV, pp.1-22, 2004.

C. Romane, , 2015.

F. Chollet, Xception : Deep learning with depthwise separable convolutions, 2016.

H. Chatoux, F. Lecellier, and C. Fernandez-maloigne, Comparative study of descriptors with dense key points, 23rd International Conference on Pattern Recognition, 1988.
URL : https://hal.archives-ouvertes.fr/hal-01461562

R. Cong, J. Lei, H. Fu, M. M. Cheng, W. Lin et al., Review of visual saliency detection with comprehensive information, 2018.

M. Calonder, V. Lepetit, C. Strecha, and P. Fua, Brief : Binary robust independent elementary features, Computer Vision-ECCV 2010, pp.778-792, 2010.

P. Cheng, W. Liu, Y. Zhang, and H. Ma, Loco : Local context based faster rcnn for small traffic sign detection, MultiMedia Modeling, pp.329-341, 2018.

J. Chen and C. W. Ngo, Deep-based ingredient recognition for cooking recipe retrieval, Proceedings of the 2016 ACM on Multimedia Conference, MM '16, pp.32-41, 2016.

J. Chen, L. Pang, and C. W. Ngo, Cross-modal recipe retrieval : How to cook this dish ?, MultiMedia Modeling, pp.588-600, 2017.

Q. Chen, Z. Song, J. Dong, Z. Huang, Y. Hua et al., Contextualizing object detection and classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.37, issue.1, pp.13-27, 2015.

T. Chen, K. H. Yap, and D. Zhang, Discriminative soft bag-of-visual phrase for mobile landmark recognition, IEEE Transactions on Multimedia, vol.16, issue.3, pp.612-622, 2014.

S. A. Chatzichristofis, K. Zagoris, Y. S. Boutalis, and N. Papamarkos, Accurate image retrieval based on compact composite descriptors and relevance feedback information, International Journal of Pattern Recognition and Artificial Intelligence, vol.24, issue.02, pp.207-244, 2010.

I. Dagan and S. P. Engelson, Committee-based sampling for training probabilistic classifiers, Machine Learning Proceedings, pp.150-157, 1995.

, Deep dream generator, 2018.

J. Delhumeau, P. H. Gosselin, H. Jégou, and P. Pérez, Revisiting the VLAD image representation, ACM Multimedia, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00840653

S. Dasgupta, D. J. Hsu, and C. Monteleoni, A general agnostic active learning algorithm, Advances in neural information processing systems, pp.353-360, 2008.

O. Day and T. M. Khoshgoftaar, A survey on heterogeneous transfer learning, Journal of Big Data, vol.4, issue.1, p.29, 2017.

M. Ducoffe and F. Precioso, Adversarial active learning for deep networks : a margin based approach, 2018.

N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol.1, pp.886-893, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00548512

C. Eggert, S. Romberg, and R. Lienhart, Improving vlad : Hierarchical coding and a refined local coordinate system, 2014 IEEE International Conference on Image Processing (ICIP), pp.3018-3022, 2014.

M. Everingham, L. Van-gool, C. K. Williams, J. Winn, and A. Zisserman, The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results, 2012.

J. Fournier, M. Cord, and S. Philipp-foliguet, Back-propagation algorithm for relevance feedback in image retrieval, Image Processing, vol.1, pp.686-689, 2001.

I. Felci-rajam and S. Valli, Content-based image retrieval using a quick svmbinary decision tree-qsvmbdt, Advances in Digital Image Processing and Information Technology, pp.11-22, 2011.

A. Gordo, J. Almazán, J. Revaud, and D. Larlus, Deep image retrieval : Learning global representations for image search, European Conference on Computer Vision, pp.241-257, 2016.

X. Glorot, A. Bordes, and Y. Bengio, Deep sparse rectifier neural networks, Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp.315-323, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00752497

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 2016.

S. Gbehounou, Image database indexing : Emotional impact evaluation. Theses, 2014.
URL : https://hal.archives-ouvertes.fr/tel-01089308

P. H. Gosselin and M. Cord, Active learning methods for interactive image retrieval, IEEE Transactions on Image Processing, vol.17, issue.7, pp.1200-1211, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00520292

G. Griffin, A. Holub, and P. Perona, Caltech-256 object category dataset, 2007.

E. Giouvanakis, C. Kotropoulos, ;. E. Gibson, W. Li, C. Sudre et al., Niftynet : a deep-learning platform for medical imaging, Saliency map driven image retrieval combining the bag-of-words model and plsa, vol.158, pp.113-122, 2014.

G. Gando, T. Yamada, H. Sato, S. Oyama, and M. Kurihara, Fine-tuning deep convolutional neural networks for distinguishing illustrations from photographs, Expert Systems with Applications, vol.66, pp.295-301, 2016.

W. E. Hart, M. Goldbaum, B. Côté, P. Kube, and M. R. Nelson, Measurement and classification of retinal vascular tortuosity, International journal of medical informatics, vol.53, issue.2-3, pp.239-252, 1999.

J. Harel, C. Koch, and P. Perona, Graph-based visual saliency, Advances in neural information processing systems, pp.545-552, 2006.

G. E. Hinton, S. Osindero, and Y. W. Teh, A fast learning algorithm for deep belief nets, Neural computation, vol.18, issue.7, pp.1527-1554, 2006.

G. E. Hinton and T. J. Sejnowski, Parallel distributed processing : Explorations in the microstructure of cognition, chapter Learning and Relearning in Boltzmann Machines, vol.1, pp.282-317, 1986.

C. Harris and M. Stephens, A combined corner and edge detector, Proc. of Fourth Alvey Vision Conference, pp.147-151, 1988.

X. Huang, C. Shen, X. Boix, and Q. Zhao, Salicon : Reducing the semantic gap in saliency prediction by adapting deep neural networks, 2015 IEEE International Conference on Computer Vision (ICCV), pp.262-270, 2015.

S. Huang, W. Wang, and H. Zhang, Retrieving images using saliency detection and graph matching, 2014 IEEE International Conference on Image Processing (ICIP), pp.3087-3091, 2014.

G. E. Hinton and R. S. Zemel, Autoencoders, minimum description length and helmholtz free energy, Advances in neural information processing systems, pp.3-10, 1994.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

L. Itti, C. Koch, and E. Niebur, A model of saliency-based visual attention for rapid scene analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.11, pp.1254-1259, 1998.

S. Iizuka, E. Simo-serra, and H. Ishikawa, Globally and locally consistent image completion, ACM Trans. Graph, vol.36, issue.4, 2017.

P. Jaccard, Distribution de la flore alpine dans le bassin des dranses et dans quelques régions voisines, vol.37, p.1901

H. Jégou, M. Douze, and C. Schmid, Hamming embedding and weak geometric consistency for large scale image search, European Conference on Computer Vision, volume I of LNCS, pp.304-317, 2008.

H. Jégou, M. Douze, C. Schmid, and P. Pérez, Aggregating local descriptors into a compact image representation, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.3304-3311, 2010.

X. Jin and J. C. French, Improving image retrieval effectiveness via multiple queries. Multimedia tools and applications, vol.26, pp.221-245, 2005.

J. Jin, K. Fu, and C. Zhang, Traffic sign recognition with hinge loss trained convolutional neural networks, IEEE Transactions on Intelligent Transportation Systems, vol.15, issue.5, 1991.

L. Juan and O. Gwun, A comparison of sift, pca-sift and surf, International Journal of Image Processing (IJIP), vol.3, issue.4, pp.143-152, 2009.

S. I. Jung and K. S. Hong, Deep network aided by guiding network for pedestrian detection, Pattern Recognition Letters, vol.90, pp.43-49, 2017.

M. Jiang, S. Huang, J. Duan, and Q. Zhao, Salicon : Saliency in context, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1072-1080, 2015.

S. Jetley, N. Murray, and E. Vig, End-to-end saliency mapping via probability distribution prediction, Proceedings of Computer Vision and Pattern Recognition, pp.5753-5761, 2016.

S. Kullback and R. A. Leibler, On Information and Sufficiency. Number, vol.1, 1951.

M. Koskela, J. Laaksonen, and E. Oja, Implementing relevance feedback as convolutions of local neighborhoods on self-organizing maps, International Conference on Artificial Neural Networks, pp.981-986, 2002.

H. Kamyshanska and R. Memisevic, The potential energy of an autoencoder, IEEE transactions on pattern analysis and machine intelligence, vol.37, pp.1261-1273, 2015.

L. Kaufmann and P. Rousseeuw, Clustering by means of medoids, vol.01, pp.405-416, 1987.

R. D. King, J. Rowland, S. G. Oliver, M. Young, W. Aubrey et al., The automation of science, Science, vol.324, issue.5923, pp.85-89, 2009.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Proceedings of the 25th International Conference on Neural Information Processing Systems, 2012.

C. Koch and S. Ullman, Shifts in selective visual attention : towards the underlying neural circuitry, Matters of intelligence, pp.115-141, 1987.

M. Kümmerer, T. Wallis, and M. Bethge, Deepgaze ii : Reading fixations from deep features trained on object recognition, 2016.

L. Hien-phuong, Towards an interactive index structuring system for content-based image retrieval in large image databases. (Vers un système interactif de structuration des index pour une recherche par le contenu dans des grandes bases d'images), 2013.

K. Lang and E. Baum, Query learning can work poorly when a human oracle is used, Proceedings of the IEEE International Joint Conference on Neural Networks, vol.2, pp.335-340, 1992.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, issue.11, 1998.

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.521, p.7553, 2015.

P. , L. Callet, and J. Benois-pineau, Visual Content Indexing and Retrieval with Psycho-Visual Models, Visual Content Indexing and Retrieval with Psycho-visual models, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01674941

S. Leutenegger, M. Chli, and R. Y. Siegwart, Brisk : Binary robust invariant scalable keypoints, 2011 International Conference on Computer Vision, pp.2548-2555, 2011.

C. Li, C. Deng, N. Li, W. Liu, X. Gao et al., Self-supervised adversarial hashing networks for cross-modal retrieval, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.4242-4251, 2018.

D. D. Lewis and W. A. Gale, A sequential algorithm for training text classifiers, Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, pp.3-12, 1994.

T. Lindeberg and J. Garding, Shape-adapted smoothing in estimation of 3-d shape cues from affine deformations of local 2-d brightness structure, Image and Vision Computing, vol.15, issue.6, pp.415-434, 1997.

A. Lechervy, P. H. Gosselin, and F. Precioso, Boosting actif pour la recherche interactive d'images, Reconnaissance des Formes et Intelligence Artificielle, p.1, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00520319

H. Lee, R. Grosse, R. Ranganath, A. Y. Ng, H. T. Le et al., Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.503-506, 2009.

G. Levi and T. Hassner, LATCH : learned arrangements of three patch codes, 2015.

N. Liu and J. Han, Dhsnet : Deep hierarchical saliency network for salient object detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.678-686, 2016.

T. Lindeberg, Feature detection with automatic scale selection, vol.30, pp.77-116, 1998.

S. Litayem, A. Joly, and N. Boujemaa, Interactive objects retrieval with efficient boosting, Proceedings of the 17th ACM international conference on Multimedia, pp.545-548, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00724876

F. R. López, H. Jiménez-salazar, and D. Pinto, A competitive term selection method for information retrieval, Computational Linguistics and Intelligent Text Processing, pp.468-475, 2007.

S. Lloyd, Least squares quantization in pcm, IEEE Transactions on Information Theory, vol.28, issue.2, pp.129-137, 1982.

J. Li, X. Liang, S. Shen, T. Xu, J. Feng et al., Scale-aware fast r-cnn for pedestrian detection, IEEE Transactions on Multimedia, vol.20, issue.4, pp.985-996, 2018.

J. Liu, F. Meng, F. Mu, and Y. Zhang, An improved image retrieval method based on sift algorithm and saliency map, 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp.766-770, 2014.

D. G. Lowe, Object recognition from local scale-invariant features, Proceedings of the International Conference on Computer Vision, vol.2, p.1150, 1999.

A. Leibetseder, M. J. Primus, and K. Schoeffmann, Automatic smoke classification in endoscopic video, MultiMedia Modeling, pp.362-366, 2018.

G. Liu, F. A. Reda, K. J. Shih, T. C. Wang, A. Tao et al., Image inpainting for irregular holes using partial convolutions, 2018.

Y. Lecun and F. Soulie-fogelman, Modèles connexionnistes de l'apprentissage, vol.01, 1987.

T. Fei, K. M. Liu, Z. Ting, ;. H. Zhou, T. Le et al., Improving retrieval framework using information gain models. Signal, Image and Video Processing, Improving retrieval framework using information gain models. Signal, Image and Video Processing, vol.11, pp.309-316, 2008.

G. Li and Y. Yu, Visual saliency based on multiscale deep features. CoRR, abs/1503.08663, 2015.

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol.1, pp.281-297, 1967.

G. J. Mclachlan and K. E. Basford, Mixture models : Inference and applications to clustering, Marcel Dekker, vol.84, 1988.

Z. Y. Ming, J. Chen, Y. Cao, C. Forde, C. W. Ngo et al., Food photo recognition for dietary tracking : System and experiment, MultiMedia Modeling, pp.129-141, 2018.

Q. Meng, D. R. Catchpoole, D. B. Skillicorn, and P. J. Kennedy, Relational autoencoder for feature extraction, 2018.
DOI : 10.1109/ijcnn.2017.7965877
URL : http://arxiv.org/pdf/1802.03145

P. Muneesawang and L. Guan, An interactive approach for cbir using a network of radial basis functions, IEEE Transactions on multimedia, vol.6, issue.5, pp.703-716, 2004.
DOI : 10.1109/tmm.2004.834866

T. M. Mitchell, Generalization as search, Readings in artificial intelligence, pp.517-542, 1981.
DOI : 10.1016/0004-3702(82)90040-6

S. Murala, R. P. Maheshwari, and R. Balasubramanian, Local tetra patterns : A new feature descriptor for content-based image retrieval, IEEE Transactions on Image Processing, vol.21, issue.5, pp.2874-2886, 2012.
DOI : 10.1109/tip.2012.2188809

J. Masci, U. Meier, D. Cire?an, and J. Schmidhuber, Stacked convolutional auto-encoders for hierarchical feature extraction, International Conference on Artificial Neural Networks, pp.52-59, 2011.
DOI : 10.1007/978-3-642-21735-7_7
URL : http://www.idsia.ch/~juergen/icann2011stack.pdf

A. K. Mccallumzy and K. Nigamy, Employing em and pool-based active learning for text classification, Proc. International Conference on Machine Learning (ICML), pp.359-367, 1998.

R. Moskovitch, N. Nissim, D. Stopel, C. Feher, R. Englert et al., Improving the detection of unknown computer worms activity using active learning, Annual Conference on Artificial Intelligence, pp.489-493

. Springer, , 2007.

H. P. Morevec, Towards automatic visual obstacle avoidance, Proceedings of the 5th International Joint Conference on Artificial Intelligence, vol.2, pp.584-584, 1977.

A. Mordvintsev, C. Olah, and M. Tyka, Deepdream-a code example for visualizing neural networks, Google Res, vol.2, 2015.

W. S. Mcculloch and W. Pitts, A logical calculus of the ideas immanent in nervous activity, The bulletin of mathematical biophysics, vol.5, issue.4, pp.115-133, 1943.

J. L. Mcclelland, D. E. Rumelhart, and G. E. Hinton, Computation &amp

, The Appeal of Parallel Distributed Processing, pp.305-341, 1995.

K. Mikolajczyk and C. Schmid, Indexing based on scale invariant interest points, International Conference on Computer Vision (ICCV '01), vol.1, pp.525-531, 2001.
DOI : 10.1109/iccv.2001.937561
URL : https://hal.archives-ouvertes.fr/inria-00548276

K. Mikolajczyk and C. Schmid, A performance evaluation of local descriptors, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.2, 2003.
DOI : 10.1109/cvpr.2003.1211478
URL : https://hal.archives-ouvertes.fr/inria-00548227

K. Mikolajczyk and C. Schmid, Scale &amp ; affine invariant interest point detectors, Int. J. Comput. Vision, vol.60, issue.1, pp.63-86, 2004.
DOI : 10.1023/b:visi.0000027790.02288.f2

K. Mikolajczyk and C. Schmid, A performance evaluation of local descriptors, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.10, pp.1615-1630, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00548227

F. Mindru, T. Tuytelaars, L. Van-gool, and T. Moons, Moment invariants for recognition under changing viewpoint and illumination, Comput. Vis. Image Underst, vol.94, issue.1-3, pp.3-27, 2004.
DOI : 10.1016/j.cviu.2003.10.011

D. Michaud, T. Urruty, P. Carré, and F. Lecellier, Adaptive features selection for expert datasets : A cultural heritage application, Signal Processing : Image Communication, vol.67, pp.161-170, 2018.
DOI : 10.1016/j.image.2018.06.011
URL : https://hal.archives-ouvertes.fr/hal-01917047

D. Michaud, T. Urruty, F. Lecellier, and P. Carré, Adaptive image representation using information gain and saliency : Application to cultural heritage datasets, MultiMedia Modeling-24th International Conference, MMM 2018, pp.54-66, 2018.
DOI : 10.1007/978-3-319-73603-7_5
URL : https://hal.archives-ouvertes.fr/hal-01917088

O. Muratov, Visual saliency detection and its application to image retrieval, 2013.

. Bibliothèque-nationale-de-france,

D. Nistér and H. Stewénius, Scalable recognition with a vocabulary tree, IEEE Conference on Computer Vision and Pattern Recognition, vol.2, 2006.

V. Nguyen, N. Vu, H. Phan, and P. H. Gosselin, An integrated descriptor for texture classification, 2016 23rd International Conference on Pattern Recognition (ICPR), pp.2006-2011, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01593389

T. Ojala, M. Pietikäinen, and D. Harwood, A comparative study of texture measures with classification based on featured distributions, Pattern Recognition, vol.29, issue.1, pp.51-59, 1996.

A. Oliva and A. Torralba, Modeling the shape of the scene : A holistic representation of the spatial envelope, International Journal of Computer Vision, vol.42, issue.3, pp.145-175, 2001.

, Bibliographie 169

A. Papushoy and A. G. Bors, Visual attention for content based image retrieval, 2015 IEEE International Conference on Image Processing (ICIP), pp.971-975, 2015.

D. Picard, P. H. Gosselin, and M. C. Gaspard, Challenges in content-based image indexing of cultural heritage collections, IEEE Signal Processing Magazine, vol.32, issue.4, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01164409

N. Pittaras, F. Markatopoulou, V. Mezaris, and I. Patras, Comparison of Fine-Tuning and Extension Strategies for Deep Convolutional Neural Networks, pp.102-114, 2017.

M. J. Primus, D. Putzgruber-adamitsch, M. Taschwer, B. Münzer, Y. Elshabrawi et al., Frame-based classification of operation phases in cataract surgery videos, MultiMedia Modeling, pp.241-253, 2018.

J. Pan, E. Sayrol, X. Giro-i-nieto, K. Mcguinness, and N. E. O'connor, Shallow and deep convolutional networks for saliency prediction, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.598-606, 2016.

F. Perronnin, J. Sánchez, and T. Mensink, Improving the fisher kernel for large-scale image classification, Proceedings of the 11th European Conference on Computer Vision : Part IV, ECCV'10, pp.143-156, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00548630

G. Pedrosa and A. Traina, From bag-of-visual-words to bag-of-visualphrases using n-grams, Graphics, Patterns and Images, 2013 26th SIBGRAPI-Conference, pp.304-311, 2013.

L. Perez and J. Wang, The effectiveness of data augmentation in image classification using deep learning, 2017.

S. J. Pan and Q. Yang, A survey on transfer learning, IEEE Transactions on Knowledge and Data Engineering, vol.22, issue.10, pp.1345-1359, 2010.

Y. Ren, J. Benois-pineau, and A. Bugeau, A comparative study of irregular pyramid matching in bag-of-bags of words model for image retrieval, Image and Signal Processing-6th International Conference, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00992335

E. Rosten, T. Drummond-;-o.-russakovsky, J. Deng, H. Su, J. Krause et al., Machine learning for high-speed corner detection, Proceedings of the 9th European Conference on Computer Vision-Volume Part I, ECCV'06, vol.115, pp.211-252, 2006.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning internal representations by error propagation, 1986.

C. J. Van-rijsbergen, Information Retrieval. Butterworth-Heinemann, 1979.

K. Stephen-e-robertson, Relevance weighting of search terms, Journal of the American Society for Information science, vol.27, issue.3, pp.129-146, 1976.

N. Roy and A. Mccallum, Toward optimal active learning through monte carlo estimation of error reduction, ICML, pp.441-448, 2001.

R. Raoui-outach, C. Million-rousseau, A. Benoit, and P. Lambert, Deep learning for automatic sale receipt understanding, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01654191

F. Rosenblatt, The perceptron : A probabilistic model for information storage and organization in the brain, Psychological Review, pp.65-386, 1958.

E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, Orb : An efficient alternative to sift or surf, 2011 International Conference on Computer Vision, pp.2564-2571, 2011.

F. Radenovi?, G. Tolias, and O. Chum, Cnn image retrieval learns from bow : Unsupervised fine-tuning with hard examples, European conference on computer vision, pp.3-20, 2016.

Y. Rubner, C. Tomasi, and L. J. Guibas, The earth mover's distance as a metric for image retrieval, International Journal of Computer Vision, vol.40, issue.2, pp.99-121, 2000.

H. Ranganathan, H. Venkateswara, S. Chakraborty, and S. Panchanathan, Deep active learning for image classification, 2017 IEEE International Conference on Image Processing (ICIP), pp.3934-3938, 2017.

S. E. Robertson, S. Walker, and M. Beaulieu, Experimentation as a way of life : Okapi at trec. Information processing & management, vol.36, pp.95-108, 2000.

G. Salton and C. Buckley, Term-weighting approaches in automatic text retrieval, Information Processing and Management, pp.513-523, 1988.

M. J. Swain and D. H. Ballard, Color indexing, International Journal of Computer Vision, vol.7, issue.1, pp.11-32, 1991.

S. M. Smith and M. A. Brady, Susan-a new approach to low level image processing, International Journal of Computer Vision, vol.23, pp.45-78, 1997.

B. Safadi, N. Derbas, and G. Quénot, Descriptor optimization for multimedia indexing and retrieval, Multimedia Tools and Applications, vol.74, issue.4, pp.1267-1290, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00953090

B. Settles, Active learning, Synthesis Lectures on Artificial Intelligence and Machine Learning, vol.6, issue.1, pp.1-114, 2012.

C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, Inception-v4, inceptionresnet and the impact of residual connections on learning, AAAI, vol.4, p.12, 2017.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed et al., Going deeper with convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.

B. Safadi and G. Quénot, A factorized model for multiple SVM and multilabel classification for large scale multimedia indexing, 13th International Workshop on Content-Based Multimedia Indexing, CBMI 2015, pp.1-6, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01230720

E. Salahat and M. Qasaimeh, Recent advances in features extraction and description algorithms : A comprehensive survey, IEEE International Conference on Industrial Technology (ICIT), pp.1059-1063, 2017.

B. Schauerte and R. Stiefelhagen, How the distribution of salient objects in images influences salient object detection, 2013 IEEE International Conference on Image Processing, pp.74-78, 2013.

O. Sener, S. Savarese, ;. Szegedy, V. Vanhoucke, S. Ioffe et al., A geometric approach to active learning for convolutional neural networks, 2015.

P. Eich and S. Von-reden, The coin collection of the seminar for ancient history, albert-ludwigs university

J. Sivic and A. Zisserman, Video google : a text retrieval approach to object matching in videos, Proceedings Ninth IEEE International Conference on Computer Vision, vol.2, pp.1470-1477, 2003.

K. Simonyan and A. Zisserman, Very deep convolutional networks for largescale image recognition, 2014.

S. Tong and E. Chang, Support vector machine active learning for image retrieval, Proceedings of the ninth ACM international conference on Multimedia, pp.107-118, 2001.

R. Tanno, T. Ege, and K. Yanai, Ar deepcaloriecam : An ios app for food calorie estimation with augmented reality, MultiMedia Modeling, pp.352-356, 2018.

A. M. Treisman and G. Gelade, A feature-integration theory of attention, Cognitive Psychology, vol.12, issue.1, pp.97-136, 1980.

Q. Tian, P. Hong, and T. S. Huang, Update relevant image weights for content-based image retrieval using support vector machines, IEEE International Conference on, vol.2, pp.1199-1202, 2000.

E. Tola, V. Lepetit, and P. Fua, Daisy : An efficient dense descriptor applied to wide-baseline stereo, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.5, pp.815-830, 2010.

T. Tuytelaars and K. Mikolajczyk, Local invariant feature detectors : A survey, Found. Trends. Comput. Graph. Vis, vol.3, issue.3, pp.177-280, 2008.

X. Tan and B. Triggs, Enhanced local texture feature sets for face recognition under difficult lighting conditions, IEEE Transactions on Image Processing, vol.19, issue.6, pp.1635-1650, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00548674

K. Tieu and P. Viola, Boosting image retrieval, International Journal of Computer Vision, vol.56, issue.1-2, pp.17-36, 2004.

H. Tian, Y. Zhao, R. Ni, L. Qin, and X. Li, Ldft-based watermarking resilient to local desynchronization attacks, vol.43, p.2013

Y. Uchida-;-t, S. Urruty, H. T. Gbèhounou, J. Le, C. Martinet et al., Iterative random visual word selection, Local feature detectors, descriptors, and image representations : A survey. CoRR, vol.249, pp.249-249, 2014.

V. Vapnik, Statistical learning theory, 1998.

K. Van-de-sande, T. Gevers, and C. Snoek, Evaluating color descriptors for object and scene recognition, IEEE Trans. Pattern Anal. Mach. Intell, vol.32, issue.9, pp.1582-1596, 2010.

C. Nader-vasconcelos and B. Vasconcelos, Increasing deep learning melanoma classification by classical and expert knowledge based image transforms, p.1, 2017.

Z. Wen, J. Gao, R. Luo, and H. Wu, Image retrieval based on saliency attention, Foundations of Intelligent Systems, pp.177-188, 2014.

C. Sebastien, A. Wong, V. Gatt, M. Stamatescu, and . Mcdonnell, Understanding data augmentation for classification : when to warp ?, 2016.

B. Widrow, M. E. Hoff, and ;. Labs, Adaptive switching circuits, 1960.

K. Weiss, T. M. Khoshgoftaar, D. D. Wang, ;. Wu, Y. Liu et al., Traffic sign detection based on convolutional neural networks, The 2013 International Joint Conference on, vol.3, pp.1-7, 2013.

J. Z. Wang, J. Li, and G. Wiederhold, Simplicity : semantics-sensitive integrated matching for picture libraries, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.9, pp.947-963, 2001.

L. Wang, X. Li, P. Xue, and K. Chan, A novel framework for svm-based image retrieval on large databases, Proceedings of the 13th annual ACM international conference on Multimedia, pp.487-490, 2005.

Y. Wu, H. Liu, J. Yuan, and Q. Zhang, Is visual saliency useful for contentbased image retrieval ? Multimedia Tools and Applications, 2017.

I. Wichakam, T. Panboonyuen, C. Udomcharoenchaikit, and P. Vateekul, Real-time polyps segmentation for colonoscopy video frames using compressed fully convolutional network, MultiMedia Modeling, pp.393-404, 2018.

K. Wu, K. H. Yap, and L. P. Chau, Region-based image retrieval using radial basis function network, 2006 IEEE International Conference on Multimedia and Expo, pp.1777-1780, 2006.

K. Wang, Q. Yin, W. Wang, S. Wu, and L. Wang, A comprehensive survey on cross-modal retrieval, 2016.

B. Wang, Y. Yang, X. Xu, A. Hanjalic, and H. T. Shen, Adversarial crossmodal retrieval, Proceedings of the 2017 ACM on Multimedia Conference, pp.154-162, 2017.

K. Wang, D. Zhang, Y. Li, R. Zhang, and L. Lin, Cost-effective active learning for deep image classification, 2017.

]. Y. Wei, Y. Zhao, C. Lu, S. Wei, L. Liu et al., Cross-modal retrieval with cnn visual features : A new baseline, IEEE transactions on cybernetics, vol.47, issue.2, pp.449-460, 2017.

Y. Xu, R. Jia, L. Mou, G. Li, Y. Chen et al., Improved relation classification by deep recurrent neural networks with data augmentation, 2016.

R. Xu, D. Wunsch, and . Clustering, , 2009.

E. Yildizer, A. Balci, M. Hassan, and R. Alhajj, Efficient contentbased image retrieval using multiple support vector machines ensemble, Expert Systems with Applications, vol.39, issue.3, pp.2385-2396, 2012.

J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, How transferable are features in deep neural networks ? CoRR, 2014.

A. B. Yandex and V. Lempitsky, Aggregating local deep features for image retrieval, 2015 IEEE International Conference on Computer Vision (ICCV), pp.1269-1277, 2015.

J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu et al., Generative image inpainting with contextual attention, 2018.

J. Yang, Q. Li, and Y. Zhuang, Image retrieval and relevance feedback using peer indexing, Multimedia and Expo, 2002. ICME'02. Proceedings. 2002 IEEE International Conference on, vol.2, pp.409-412, 2002.

C. Zhang and T. Chen, An active learning framework for content-based information retrieval, IEEE transactions on multimedia, vol.4, issue.2, pp.260-268, 2002.

C. Zhang and X. Chen, Region-based image clustering and retrieval using multiple instance learning, International Conference on Image and Video Retrieval, pp.194-204, 2005.

K. Zagoris, S. A. Chatzichristofis, N. Papamarkos, and Y. S. Boutalis, Automatic image annotation and retrieval using the joint composite descriptor, 14th Panhellenic Conference on Informatics, pp.143-147, 2010.

Z. Zdziarski and R. Dahyot, Feature selection using visual saliency for content-based image retrieval, Signals and Systems Conference, pp.1-6, 2012.

M. D. Zeiler and R. Fergus, Visualizing and understanding convolutional networks. CoRR, abs, 1311.

B. Zhang, Y. Gao, S. Zhao, and J. Liu, Local derivative pattern versus local binary pattern : Face recognition with high-order local pattern descriptor, IEEE Transactions on Image Processing, vol.19, issue.2, pp.533-544, 2010.

S. Zagoruyko and N. Komodakis, Wide residual networks, Proceedings of the British Machine Vision Conference, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01832503

L. Zhang, L. Lin, X. Liang, and K. He, Is faster R-CNN doing well for pedestrian detection ? CoRR, 2016.

R. Zhao, W. Ouyang, H. Li, X. Wang, ;. Zhang et al., Sun : A bayesian framework for saliency using natural statistics, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol.8, pp.1265-1274, 2008.