On improving robustness of LDA and SRDA by using tangent vectors

This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters, [Volume 34, Issue 9, 1 July 2013, Pages 1094–1100] DOI: 10.1016/j.patrec.2013.03.001[EN] In the area of pattern recognition, it is common for few training samples to be available with respect to the dimensionality of the representation space; this is known as the curse of dimensionality. This problem can be alleviated by using a dimensionality reduction approach, which overcomes the curse relatively well. Moreover, supervised dimensionality reduction techniques generally provide better recognition performance; however, several of these tend to suffer from the curse when applied directly to high-dimensional spaces. We propose to overcome this problem by incorporating additional information to supervised subspace learning techniques using what is known as tangent vectors. This additional information accounts for the possible differences that the sample data can suffer. In fact, this can be seen as a way to model the unseen data and make better use of the scarce training samples. In this paper, methods for incorporating tangent vector information are described for one classical technique (LDA) and one state-of-the-art technique (SRDA). Experimental results confirm that this additional information improves performance and robustness to known transformations.Work partially supported through the EU 7th Framework Programme grant tranScriptorium (Ref: 600707), by the Spanish MEC under the STraDA research project (TIN2012-37475-C02-01) and by the Generalitat Valenciana under grant Prometeo/2009/014.Villegas Santamaría, M.; Paredes Palacios, R. (2013). On improving robustness of LDA and SRDA by using tangent vectors. Pattern Recognition Letters. 34(9):1094-1100. https://doi.org/10.1016/j.patrec.2013.03.0011094110034

Passive-Aggressive online learning with nonlinear embeddings

[EN] Nowadays, there is an increasing demand for machine learning techniques which can deal with problems where the instances are produced as a stream or in real time. In these scenarios, online learning is able to learn a model from data that comes continuously. The adaptability, efficiency and scalability of online learning techniques have been gaining interest last years with the increasing amount of data generated every day. In this paper, we propose a novel binary classification approach based on nonlinear mapping functions under an online learning framework. The non-convex optimization problem that arises is split into three different convex problems that are solved by means of Passive-Aggressive Online Learning. We evaluate both the adaptability and generalization of our model through several experiments comparing with the state of the art techniques. We improve significantly the results in several datasets widely used previously by the online learning community. (C) 2018 Elsevier Ltd. All rights reserved.This work was developed in the framework of the PROM-ETEOII/2014/030 research project "Adaptive learning and multi modality in machine translation and text transcription", funded by the Generalitat Valenciana. The work of the first author is financed by Grant FPU14/03981, from the Spanish Ministry of Education, Culture and Sport.Jorge-Cano, J.; Paredes Palacios, R. (2018). Passive-Aggressive online learning with nonlinear embeddings. Pattern Recognition. 79:162-171. https://doi.org/10.1016/j.patcog.2018.01.019S1621717

Study of Convolutional Neural Networks for Global Parametric Motion Estimation on Log-Polar Imagery

[EN] The problem of motion estimation from images has been widely studied in the past. Although many mature solutions exist, there are still open issues and challenges to be addressed. For instance, in spite of the well-known performance of convolutional neural networks (CNNs) in many computer vision problems, only very recent work has started to explore CNNs to learning to estimate motion, as an alternative to manually-designed algorithms. These few initial efforts, however, have focused on conventional Cartesian images, while other imaging models have not been studied. This work explores the yet unknown role of CNNs in estimating global parametric motion in log-polar images. Despite its favourable properties, estimating some motion components in this model has proven particularly challenging with past approaches. It is therefore highly important to understand how CNNs behave when their input are log-polar images, since they involve a complex mapping in the motion model, a polar image geometry, and space-variant resolution. To this end, a CNN is considered in this work for regressing the motion parameters. Experiments on existing image datasets using synthetic image deformations reveal that, interestingly, standard CNNs can successfully learn to estimate global parametric motion on log-polar images with accuracies comparable to or better than with Cartesian images.This work was supported in part by the Universitat Jaume I, Castellon, Spain, through the Pla de promocio de la investigacio, under Project UJI-B2018-44; and in part by the Spanish Ministerio de Ciencia, Innovacion y Universidades through the Research Network under Grant RED2018-102511-T.Traver, VJ.; Paredes Palacios, R. (2020). Study of Convolutional Neural Networks for Global Parametric Motion Estimation on Log-Polar Imagery. IEEE Access. 8:149122-149132. https://doi.org/10.1109/ACCESS.2020.3016030S149122149132

Local Deep Neural Networks for gender recognition

Deep learning methods are able to automatically discover better representations of the data to improve the performance of the classifiers. However, in computer vision tasks, such as the gender recognition problem, sometimes it is difficult to directly learn from the entire image. In this work we propose a new model called Local Deep Neural Network (Local-DNN), which is based on two key concepts: local features and deep architectures. The model learns from small overlapping regions in the visual field using discriminative feed forward networks with several layers. We evaluate our approach on two well-known gender benchmarks, showing that our Local-DNN outperforms other deep learning methods also evaluated and obtains state-of-the-art results in both benchmarks. (C) 2015 Elsevier B.V. All rights reserved.This work was financially supported by the Ministerio de Ciencia e Innovacin (Spain), Plan Nacional de I-D+i, TEC2009-09146, and the FPI grant BES-2010-032945.Mansanet Sandín, J.; Albiol Colomer, A.; Paredes Palacios, R. (2016). Local Deep Neural Networks for gender recognition. Pattern Recognition Letters. 70:80-86. https://doi.org/10.1016/j.patrec.2015.11.015S80867

Feature representation for social circles detection using MAC

The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-016-2222-ySocial circles detection is a special case of community detection in social network that is currently attracting a growing interest in the research community. In this paper, we propose an empirical evaluation of the multi-assignment clustering method using different feature representation models. We define different vectorial representations from both structural egonet information and user profile features. We study and compare the performance on two available labelled Facebook datasets and compare our results with several different baselines. In addition, we provide some insights of the evaluation metrics most commonly used in the literature.This work was developed in the framework of the W911NF-14-1-0254 research project Social Copying Community Detection (SOCOCODE), funded by the US Army Research Office (ARO). The work of the first author is financed by Grant FPU14/03483, from the Spanish Ministry of Education, Culture and Sport.Alonso-Nanclares, JA.; Paredes Palacios, R.; Rosso, P. (2016). Feature representation for social circles detection using MAC. Neural Computing and Applications. 1-8. https://doi.org/10.1007/s00521-016-2222-yS18Alonso J, Paredes R, Rosso P (2015) Empirical evaluation of different feature representations for social circles detection. In: Pattern recognition and image analysis, lecture notes in computer science, vol. 9117, pp 31–38. Springer, Berlin. doi: 10.1007/978-3-319-19390-8_4Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theor Exp 2008:P10, 008Brandes U, Delling D, Gaertler M, Gaerke R, Hoefer M, Nikoloski Z, Wagner D (2006) On modularity-NP-completeness and beyond. Technical Report. 2006–19, ITI Wagner, Faculty of Informatics, Universität Karlsruhe (TH), GermanyBuhmann J, Kuhnel H (1993) Vector quantization with complexity costs. IEEE Trans Inf Theory 39(4):1133–1145Chen Y, Lin C (2006) Combining SVMs with various feature selection strategies. In: Feature extraction, pp 315–324Dey K, Bandyopadhyay S (2013) An empirical investigation of like-mindedness of topically related social communities on microblogging platforms. In: International conference on natural languagesDonath WE, Hoffman AJ (1973) Lower bounds for the partitioning of graphs. IBM J Res Dev 17(5):420–425Everitt BS, Hand DJ (1981) Finite mixture distributions. Chapman and Hall, LondonFortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174Frank M, Streich AP, Basin D, Buhmann JM (2012) Multi-assignment clustering for Boolean data. J Mach Learn Res 13(1):459–489Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Springer, BerlinJaccard P (1908) Nouvelles recherches sur la distribution florale. Bulletin de la Socit Vaudoise des Sciences Naturelles 44(163):223–270Kaggle: Learning social circles in networks. http://www.kaggle.com/c/learning-social-circlesKernighan BW, Lin S (1970) An efficient heuristic procedure for partitioning graphs. Bell Syst Tech J 49(2):291–307Leskovec J, Krevl A (2014) SNAP datasets: stanford large network dataset collection. http://snap.stanford.edu/dataLeskovec J, Mcauley J (2012) Learning to discover social circles in ego networks. In: Pereira F, Burges C, Bottou L, Weinberger K (eds) Advances in neural information processing systems, vol 25. Curran Associates, Inc., Red Hook, pp 539–547Lloyd S (1982) Least squares quantization in PCM. IEEE Trans Inf Theory 28(2):129–137MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of fifth Berkeley symposium on Mathematical Statistics and Probability, vol 1, pp 281–297McAuley J, Leskovec J (2014) Discovering social circles in ego networks. ACM Trans Knowl Discov Data 8(1):4Munkres J (1957) Algorithms for the assignment and transportation problems. J Soc Ind Appl Math 5(1):32–38Newman ME (2006) Modularity and community structure in networks. Proc Natl Acad Sci 103(23):8577–8582Newman ME, Girvan M (2014) Finding and evaluating community structure in networks. Phys Rev E Stat Nonlinear Soft Matter Phys 69(2):026,113Palla G, Derényi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043):814–818Pathak N, DeLong C, Banerjee A, Erickson K (2008) Social topic models for community extraction. 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In: Proceedings of the 12th ACM symposium on access control models and technologies, pp 175–184Yang J, McAuley J, Leskovec J (2013) Community detection in networks with node attributes. In: IEEE 13th international conference on data mining (ICDM), pp 1151–1156. IEEEZhou D, Councill I, Zha H, Giles CL (2007) Discovering temporal communities from social network documents. In: Seventh IEEE international conference on data mining, pp 745–75

Data Mapping by Restricted Boltzmann Machines for Social Circles Detection

©2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Social circles detection is a special case of community detection in social network that is currently attracting a growing interest in the research community. In this paper, we propose a two-step technique, making emphasis on the mapping of the data by Restricted Boltzmann Machines (RBMs). Social circles are subsequently inferred by k-means over the preprocessed data. We define different vectorial representations from both structural egonet information and user profile features, and perform a set of tests to adjust the optimal parameters of the RBMs. We study and compare the performance on the ego-Facebook dataset of social circles from Facebook from the Stanford Large Network Dataset Collection. We compare our results with several different baselines.This work was developed in the framework of the W911NF-14-1-0254 research project Social Copying Community Detection (SOCOCODE), funded by the US Army Research Office (ARO).Alonso Nanclares, JA.; Paredes Palacios, R.; Rosso, P. (2015). Data Mapping by Restricted Boltzmann Machines for Social Circles Detection. IEEE. https://doi.org/10.1109/IJCNN.2015.7280653

Relevant clouds: leveraging relevance feedback to build tag clouds for image search

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40802-1_18Previous work in the literature has been aimed at exploring tag clouds to improve image search and potentially increase retrieval performance. However, to date none has considered the idea of building tag clouds derived from relevance feedback. We propose a simple approach to such an idea, where the tag cloud gives more importance to the words from the relevant images than the non-relevant ones. A preliminary study with 164 queries inspected by 14 participants over a 30M dataset of automatically annotated images showed that 1) tag clouds derived this way are found to be informative: users considered roughly 20% of the presented tags to be relevant for any query at any time; and 2) the importance given to the tags correlates with user judgments: tags ranked in the first positions tended to be perceived more often as relevant to the topic that users had in mind.Work supported by EU FP7/2007-2013 under grant agreements 600707 (tranScriptorium) and 287576 (CasMaCat), and by the STraDA project (TIN2012-37475-C02-01).Leiva Torres, LA.; Villegas Santamaría, M.; Paredes Palacios, R. (2013). Relevant clouds: leveraging relevance feedback to build tag clouds for image search. En Information Access Evaluation. Multilinguality, Multimodality, and Visualization. Springer Verlag (Germany). 143-149. https://doi.org/10.1007/978-3-642-40802-1_18S143149Begelman, G., Keller, P., Smadja, F.: Automated tag clustering: Improving search and exploration in the tag space. In: Collaborative Web Tagging (2006)Callegari, J., Morreale, P.: Assessment of the utility of tag clouds for faster image retrieval. In: Proc. MIR (2010)Ganchev, K., Hall, K., McDonald, R., Petrov, S.: Using search-logs to improve query tagging. In: Proc. ACL (2012)Hassan-Montero, Y., Herrero-Solana, V.: Improving tag-clouds as visual information retrieval interfaces. In: Proc. InSciT (2006)Leiva, L.A., Villegas, M., Paredes, R.: Query refinement suggestion in multimodal interactive image retrieval. In: Proc. ICMI (2011)Liu, D., Hua, X.-S., Yang, L., Wang, M., Zhang, H.-J.: Tag ranking. In: Proc. WWW (2009)Overell, S., Sigurbjörnsson, B., van Zwol, R.: Classifying tags using open content resources. In: Proc. WSDM (2009)Rui, Y., Huang, T.S., Ortega, M., Mehrotra, S.: Relevance feedback: A power tool for interactive content-based image retrieval. T. Circ. Syst. Vid. 8(5) (1998)Sigurbjörnsson, B., van Zwol, R.: Flickr tag recommendation based on collective knowledge. In: Proc. WWW (2008)Trattner, C., Lin, Y.-L., Parra, D., Yue, Z., Real, W., Brusilovsky, P.: Evaluating tag-based information access in image collections. In: Proc. HT (2012)Villegas, M., Paredes, R.: Image-text dataset generation for image annotation and retrieval. In: Proc. CERI (2012)Zhang, C., Chai, J.Y., Jin, R.: User term feedback in interactive text-based image retrieval. In: Proc. SIGIR (2005

Overview of the ImageCLEF 2014 Scalable Concept Image Annotation Task

[EN] The ImageCLEF 2014 Scalable Concept Image Annotation task was the third edition of a challenge aimed at developing more scalable image annotation systems. Unlike traditional image annotation challenges, which rely on a set of manually annotated images as training data, the participants were only allowed to use data and/or resources that as new concepts to detect are introduced do not require significant human effort (such as hand labeling). The participants were provided with web data consisting of 500,000 images, which included textual features obtained from the web pages on which the images appeared, as well as various visual features extracted from the images themselves. To optimize their systems, the participants were provided with a development set of 1,940 samples and its corresponding hand labeled ground truth for 107 concepts. The performance of the submissions was measured using a test set of 7,291 samples which was hand labeled for 207 concepts among which 100 were new concepts unseen during development. In total 11 teams participated in the task submitting overall 58 system runs. Thanks to the larger amount of unseen concepts in the results the generalization of the systems has been more clearly observed and thus demonstrating the potential for scalability.The authors are very grateful with the CLEF initiative for supporting Image CLEF.The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under the tranScriptorium project (#600707) and from the Spanish MEC under the STraDA project (TIN2012-37475-C02-01).Villegas Santamaría, M.; Paredes Palacios, R. (2014). Overview of the ImageCLEF 2014 Scalable Concept Image Annotation Task. CEUR Workshop Proceedings. 1180:308-328. http://hdl.handle.net/10251/61152S308328118

Automatic Classification and Quantification of Basic Distresses on Urban Flexible Pavement through Convolutional Neural Networks

[EN] Pavement condition assessment is a critical step in road pavement management. In contrast to the automatic and objective methods used for rural roads, the most commonly used method in urban areas is the development of visual surveys usually filled out by technicians that leads to a subjective pavement assessment. While most previous studies on automatic identification of distresses focused on crack detection, this research aims not only to cover the identification and classification of multiple urban flexible pavement distresses (longitudinal and transverse cracking, alligator cracking, raveling, potholes, and patching), but also to quantify them through the application of Convolutional Neural Networks. Additionally, this study also proposes a methodology for an automatic pavement assessment considering the different stages developed in this research. This methodology allows for a more efficient and reliable pavement assessment, minimizing the cost and time required by the current visual surveys.The study presented in this paper is part of the research project titled SIMEPU Sistema Integral de Mantenimiento Eficiente de Pavimentos Urbanos, funded by the Spanish Ministries of Science and Innovation and Universities, as well as the European Regional Development Fund under Grant No. RTC-2017-6148-7. The authors also acknowledge the support of partner companies Pavasal Empresa Constructora, S.A. and CPS Infraestructuras, Movilidad y Medio Ambiente, S.L. and the Valencia City Council.Llopis-Castelló, D.; Paredes Palacios, R.; Parreño-Lara, M.; García-Segura, T.; Pellicer, E. (2021). Automatic Classification and Quantification of Basic Distresses on Urban Flexible Pavement through Convolutional Neural Networks. Journal of Transportation Engineering, Part B: Pavements. 147(4):1-8. https://doi.org/10.1061/JPEODX.000032118147

Restricted Boltzmann Machines for Gender Classification

This paper deals with automatic feature learning using a generative model called Restricted Boltzmann Machine (RBM) for the problem of gender recognition in face images. The RBM is presented together with some practical learning tricks to improve the learning capabilities and speedup the training process. The performance of the features obtained is compared against several linear methods using the same dataset and the same evaluation protocol. The results show a classification accuracy improvement compared with classical linear projection methods. Moreover, in order to increase even more the classification accuracy, we have run some experiments where an SVM is fed with the non-linear mapping obtained by the RBM in a tandem configuration.Mansanet Sandin, J.; Albiol Colomer, A.; Paredes Palacios, R.; Villegas, M.; Albiol Colomer, AJ. (2014). Restricted Boltzmann Machines for Gender Classification. Lecture Notes in Computer Science. 8814:274-281. doi:10.1007/978-3-319-11758-4_30S2742818814Bengio, Y., Courville, A., Vincent, P.: Representation learning: A review and new perspectives. IEEE Trans. on PAMI 35(8), 1798–1828 (2013)Bressan, M., Vitrià, J.: Nonparametric discriminant analysis and nearest neighbor classification. Pattern Recognition Letters 24(15), 2743–2749 (2003)Buchala, S., et al.: Dimensionality reduction of face images for gender classification. In: Proceedings of the Intelligent Systems, vol. 1, pp. 88–93 (2004)Cai, D., He, X., Hu, Y., Han, J., Huang, T.: Learning a spatially smooth subspace for face recognition. In: CVPR, pp. 1–7 (2007)Courville, A., Bergstra, J., Bengio, Y.: Unsupervised models of images by spike-and-slab rbms. In: ICML, pp. 1145–1152 (2011)Huang, G.B., et al.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07–49, Univ. of Massachusetts (October 2007)Schmah, T., et al.: Generative versus discriminative training of rbms for classification of fmri images. In: NIPS, pp. 1409–1416 (2008)Graf, A.B.A., Wichmann, F.A.: Gender classification of human faces. In: Bülthoff, H.H., Lee, S.-W., Poggio, T.A., Wallraven, C. (eds.) BMCV 2002. LNCS, vol. 2525, pp. 491–500. Springer, Heidelberg (2002)He, X., Niyogi, P.: Locality preserving projections. In: NIPS (2004)Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14(8), 1771–1800 (2002)Hinton, G.E.: A practical guide to training restricted boltzmann machines. Technical report, University of Toronto (2010)Hinton, G.E., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)Moghaddam, B., Yang, M.-H.: Learning gender with support faces. IEEE Trans. on PAMI 24(5), 707–711 (2002)Nair, V., Hinton, G.E.: 3d object recognition with deep belief nets. In: NIPS, pp. 1339–1347 (2009)Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted boltzmann machines for collaborative filtering. In: ICML, pp. 791–798 (2007)Shan, C.: Learning local binary patterns for gender classification on real-world face images. Pattern Recognition Letters 33(4), 431–437 (2012)Shobeirinejad, A., Gao, Y.: Gender classification using interlaced derivative patterns. In: ICPR, pp. 1509–1512 (2010)Villegas, M., Paredes, R.: Dimensionality reduction by minimizing nearest-neighbor classification error. Pattern Recognition Letters 32(4), 633–639 (2011