1,720,966 research outputs found
Classifier Selection Based On The Correlation Of Diversity Measures: When Fewer Is More
No full textThe ever-growing access to high-resolution images has prompted the development of region-based classification methods for remote sensing images. However, in agricultural applications, the recognition of specific regions is still a challenge as there could be many different spectral patterns in a same studied area. In this context, depending on the features used, different learning methods can be used to create complementary classifiers. Many researchers have developed solutions based on the use of machine learning techniques to address these problems. Examples of successful initiatives are those dedicated to the development of learning techniques for data fusion or Multiple Classifier Systems (MCS). In MCS, diversity becomes an essential factor for their success. Different works have been using diversity measures to select appropriate high-performance classifiers, but the challenge of finding the optimal number of classifiers for a target task has not been properly addressed yet. In general, the proposed solutions rely on the a priori use of ad hoc strategies for selecting classifiers, followed by the evaluation of their effectiveness results during training. Searching by the optimal number of classifiers, however, makes the selection process more expensive. In this paper, we address this issue by proposing a novel strategy for selecting classifiers to be combined based on the correlation of different diversity measures. Diversity measures are used to rank pairs of classifiers and the agreement among ranked lists guides the classifier selection process. A fusion framework has been used in our experiments targeted to the classification of coffee crops in remote sensing images. Experiment results demonstrate that the novel strategy is able to yield comparable effectiveness results when contrasted to several baselines, but using much fewer classifiers. © 2013 IEEE.1623Microsoft ResearchCastillejo-González, López-Granados, García-Ferrer, Peña-Barragán, Jurado-Expósito, De La Orden, González-Audicana, Object-and pixel-based analysis for mapping crops and their agro-environmental associated measures using quickbird imagery (2009) Elsevier Computer and Electronics in AgricultureDos Santos, J.A., Faria, F.A., Calumby, R., Da S Torres, R., Lamparelli, R., A genetic programming approach for coffee crop recognition (2010) IEEE Geoscience and Remote Sensing SymposiumPouteau, R., Stoll, B., Svm selective fusion (self) for multi-source classification of structurally complex tropical rainforest (2012) IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5 (4), pp. 1203-1212. , augDuro, D.C., Franklin, S.E., Dubé, M.G., A comparison of pixelbased and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using spot-5 hrg imagery (2012) Remote Sensing of Environment, 118, pp. 259-272Blaschke, T., Object based image analysis for remote sensing (2010) ISPRS Journal of Photogrammetry and Remote SensingDos Santos, J.A., Penatti, O.A.B., Da S Torres, R., Evaluating the potential of texture and color descriptors for remote sensing image retrieval and classification (2010) Intl. Conf. on Computer Vision Theory and Applications, pp. 203-208. , Angers, France, MayRocha, A., Papa, J., Meira, L., How far you can get using machine learning black-boxes (2010) Conf. on Graphics, Patterns and Images, pp. 193-200. , 30 2010-sept. 3Lu, D., Weng, Q., A survey of image classification methods and techniques for improving classification performance (2007) Intl. Journal of Remote Sensing, 28 (5), pp. 823-870Mountrakis, G., Im, J., Ogole, C., Support vector machines in remote sensing: A review (2011) ISPRS Journal of Photogrammetry and Remote Sensing, 66 (3), pp. 247-259Faria, F.A., Dos Santos, J.A., Torres, R.D.S., Rocha, A., Falcão, A.X., Automatic fusion of region-based classifiers for coffee crop recognition (2012) IEEE Geoscience and Remote Sensing SymposiumKuncheva, L.I., Whitaker, C.J., Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy (2003) Machine LearningShipp, C.A., Kuncheva, L.I., An investigation into how adaboost affects classifier diversity (2009) IPMU, pp. 203-208Faria, F.A., Santos, J.A., Rocha, A., Torres, R.D.S., Automatic classifier fusion for produce recognition (2012) Conf. on Graphics, Patterns and ImagesDos Santos, J.A., Faria, F.A., Torres, R.D.S., Rocha, A., Gosselin, P.-H., Philipp-Foliguet, S., Falcao, A., Descriptor correlation analysis for remote sensing image multi-scale classification (2012) Intl. Conf. on Pattern Recognition, pp. 3078-3081Du, P., Xia, J., Zhang, W., Tan, K., Liu, Y., Liu, S., Multiple classifier system for remote sensing image classification: A review (2012) Sensors, 12 (4), p. 4764Boser, B.E., Guyon, I.M., Vapnik, V.N., A training algorithm for optimal margin classifiers (1992) Workshop on Computational Learning Theory, Ser. COLT '92, pp. 144-152Cristianini, N., Shawe-Taylor, J., (2000) An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, , Cambridge University PressGuigues, L., Cocquerez, J., Le Men, H., Scale-sets image analysis (2006) Intl. Journal of Computer VisionMyneni, R.B., Hall, F.G., Sellers, P.J., Marshak, A.L., The interpretation of spectral vegetation indexes (1995) Geoscience and Remote Sensing, IEEE Transactions on, 33 (2), pp. 481-486Stehling, R., Nascimento, M., Falcao, A., A compact and efficient image retrieval approach based on border/interior pixel classification (2002) ACM Conf. on Information and Knowledge Management, pp. 102-109Pass, G., Zabih, R., Miller, J., Comparing images using color coherence vectors (1996) ACM Intl. Conf. on Multimedia, pp. 65-73Swain, M., Ballard, D., Color indexing (1991) Intl. Journal of Computer Vision, 7 (1), pp. 11-32Huang, C., Liu, Q., An orientation independent texture descriptor for image retrieval (2007) Intl. Conf. on Communications, Circuits and Systems, pp. 772-776Zegarra, J., Leite, N., Torres, R., Wavelet-based feature extraction for fingerprint image retrieval (2008) Journal of Computational and Applied Mathematics, 227 (2), pp. 294-307Unser, M., Sum and difference histograms for texture classification (1986) IEEE Transactions Pattern Analysis and Machine Intelligence, 8 (1), pp. 118-125Penatti, O.A.B., Valle, E., Torres, R.D.S., Comparative study of global color and texture descriptors for web image retrieval (2012) Journal of Visual Communication and Image RepresentationBrennan, R.L., Prediger, D.J., Coefficient kappa: Some uses, misuses, and alternatives (1981) Educational and Psychological MeasurementMa, Z., Redmond, R.L., Tau coefficients for accuracy assessment of classification of remote sensing data (1995) Photogrametric Engineering and Remote SensingKendall, M.G., A new measure of rank correlation (1938) Biometrika, 30 (1-2), pp. 81-93. , Ju
Applying Machine Learning Based On Multiscale Classifiers To Detect Remote Phenology Patterns In Cerrado Savanna Trees
No full textPlant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground. © 2013 Elsevier B.V.234961Ahrends, H., Etzold, S., Kutsch, W., Stoeckli, R., Bruegger, R., Jeanneret, F., Wanner, H., Eugster, W., Tree phenology and carbon dioxide fluxes: use of digital photography for process-based interpretation at the ecosystem scale (2009) Climate Research, 39, pp. 261-274Alberton, B., Almeida, J., Henneken, R., Torres, R.D.S., Menzel, A., Morellato, L.P.C., Near remote phenology: applying digital images to monitor leaf phenology in a Brazilian Cerrado savanna (2012) International Conference on Phenology (Phenology'12), p. 2Almeida, J., dos Santos, J.A., Alberton, B., Torres, R.D.S., Morellato, L.P.C., Remote phenology: applying machine learning to detect phenological patterns in a Cerrado savanna (2012) IEEE International Conference on eScience (eScience'12), pp. 1-8Almeida, J., dos Santos, J.A., Alberton, B., Morellato, L.P.C., Torres, R.D.S., Visual rhythm-based time series analysis for phenology studies (2013) IEEE International Conference on Image Processing (ICIP'13), pp. 1-5Alpaydin, E., (2010) Introduction to Machine Learning. Adaptive Computation and Machine Learning, , MIT PressAndrade, F.S.P., Almeida, J., Pedrini, H., Torres, R.D.S., Fusion of local and global descriptors for content-based image and video retrieval (2012) Iberoamerican Congress on, Pattern Recognition (CIARP'12), pp. 845-853Cheng, H.-D., Jiang, X., Sun, Y., Wang, J., Color image segmentation: advances and prospects (2001) Pattern Recognition, 34 (12), pp. 2259-2281Cope, J.S., Corney, D.P.A., Clark, J.Y., Remagnino, P., Wilkin, P., Plant species identification using digital morphometrics: a review (2012) Expert Systems with Applications, 39 (8), pp. 7562-7573Coutinho, L.M., O conceito de cerrado (1978) Brazilian Journal of Botany, 1, pp. 17-23Culbert, P.D., Pidgeon, A.M., St. Louis, V., Bash, D., Radeloff, V.C., The impact of phenological variation on texture measures of remotely sensed imagery (2009) IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2 (4), pp. 299-309dos Santos, J.A., Faria, F.A., Torres, R.D.S., Rocha, A., Gosselin, P.-H., Philipp-Foliguet, S., Falcão, A.X., Descriptor correlation analysis for remote sensing image multiscale classification (2012) IEEE International Conference on Pattern Recognition (ICPR'12), pp. 3078-3081dos Santos, J.A., Gosselin, P.-H., Philipp-Foliguet, S., Torres, R.D.S., Falcão, A.X., Multiscale classification of remote sensing images (2012) IEEE Transactions on Geoscience and Remote Sensing, 50 (10), pp. 3764-3775Grabner, H., Bischof, H., On-line boosting and vision (2006) IEEE International Conference on Computer Vision and, Pattern Recognition (CVPR'06), pp. 260-267Guigues, L., Cocquerez, J., Le Men, H., Scale-sets image analysis (2006) International Journal of Computer Vision, 68, pp. 289-317Haralick, R.M., Shanmugam, K., Dinstein, I., Textural features for image classification (1973) IEEE Transaction on Systems, Man and Cybernetics, 3 (6), pp. 610-621Ide, R., Oguma, H., Use of digital cameras for phenological observations (2010) Ecological Informatics, 5, pp. 339-347Keeling, C.D., Chin, J.F.S., Whorf, T.P., Increased activity of northern vegetation inferred from atmospheric co2 measurements (1996) Nature, 382, pp. 146-149Kumar, N., Belhumeur, P.N., Biswas, A., Jacobs, D.W., Kress, W.J., Lopez, I.C., Soares, J.V.B., Leafsnap: a computer vision system for automatic plant species identification (2012) European Conference on Computer Vision (ECCV'12), pp. 502-516Kurc, S., Benton, L., Digital image-derived greenness links deep soil moisture to carbon uptake in a creosotebush-dominated shrubland (2010) Journal of Arid Environments, 74, pp. 585-594Lechervy, A., Gosselin, P.-H., Precioso, F., (2013) Boosted Kernel for Image Categorization. Multimedia Tools and ApplicationsLoustau, D., Bosc, A., Colin, A., Davi, H., François, C., Dufrêne, E., Équé, M., Delage, J., Modeling the climate change effects on the potential reduction of french plains forests at the sub regional level (2005) Tree Physiology, 25, pp. 813-823Morellato, L.P.C., Rodrigues, R.R., Leitão Filho, H.F., Joly, C.A., Estudo comparativo da fenologia de espécies arbóreas de floresta de altitude e floresta mesófila semidecídua na serra do iapí, jundiaí, são paulo (1989) Brazilian Journal of Botany, 12, pp. 85-98Nagai, S., Maeda, T., Gamo, M., Muraoka, H., Suzuki, R., Nasahara, K.N., Using digital camera images to detect canopy condition of deciduous broad-leaved trees (2011) Plant Ecology and Diversity, 4, pp. 79-89Negi, G.C.S., Leaf and bud demography and shoot growth in evergreen and deciduous trees of Central Himalaya, India (2006) Trees, 20, pp. 416-429Parmesan, C., Yohe, G.A., A globally coherent fingerprint to climate change impacts accross natural systems (2003) Nature, 421, pp. 37-42Reich, P.B., Phenology of tropical forests: patterns, causes and consequences (1995) Canadian Journal of Botany, 73, pp. 164-174Reys, P., (2008) Estrutura e fenologia da vegetação de borda e interior em um fragmento de cerrado Sensu Stricto no sudeste do brasil (itirapina, são paulo), , (Ph.D. thesis), Bioscience Institute, Sao Paulo State University, Rio Claro, SP, BrazilRichardson, A.D., Jenkins, J.P., Braswell, B.H., Hollinger, D.Y., Ollinger, S.V., Smith, M.L., Use of digital webcam images to track spring greep-up in a deciduous broadleaf forest (2007) Oecologia, 152, pp. 323-334Richardson, A.D., Braswell, B.H., Hollinger, D.Y., Jenkins, J.P., Ollinger, S.V., Near-surface remote sensing of spatial and temporal variation in canopy phenology (2009) Ecological Applications, 19, pp. 1417-1428Rosenzweig, C., Karoly, D., Vicarelli, M., Neofotis, P., Wu, Q., Casassa, G., Menzel, A., Imeson, A., Attributing physical and biological impacts to anthropogenic climate change (2008) Nature, 453, pp. 353-357Rostamizadeh, A., Talwalker, A., (2012) Foundations of Machine Learning. Adaptive Computation and Machine Learning Series, , University Press Group LimitedRotzer, T., Grote, R., Pretzsch, H., The timing of bud burst and its effect on tree growth (2004) International Journal of Biometeorology, 48, pp. 109-118Schapire, R.E., A brief introduction to boosting (1999) International Joint Conference on, Artificial Intelligence (IJCAI'99), pp. 1401-1406. , T. Dean (Ed.)Schwartz, M.D., (2003) Phenology: An Integrative Environmental Science, , Academic PublishersSchwartz, M.D., Reed, B.C., White, M.A., Assessing satellite derived start-of-season measures in the coterminous (2002) International Journal of Climatology, 22, pp. 1793-1805Staggemeier, V.G., Diniz-Filho, J.F., Morellato, L.P.C., The shared influence of phylogeny and ecology on the reproductive patterns of Myrteae (Myrtaceae) (2010) Journal of Ecology, 98, pp. 1409-1421Tan, P.-N., Steinbach, M., Kumar, V., (2005) Introduction to Data Mining, , Addison-WesleyTorres, R.D.S., Falcão, A.X., Content-based image retrieval: theory and applications (2006) Revista de Informática Teórica e Aplicada, 13 (2), pp. 161-185Torres, R.D.S., Hasegawa, M., Tabbone, S., Almeida, J., dos Santos, J.A., Alberton, B., Morellato, L.P.C., Shape-based time series analysis for remote phenology studies (2013) IEEE International Geoscience and Remote Sensing Symposium (IGARSS'13), pp. 1-4Unser, M., Sum and difference histograms for texture classification (1986) IEEE Transactions on Pattern Analysis and Machine Intelligence, 8 (1), pp. 118-125Viola, P., Jones, M., Rapid object detection using a boosted cascade of simple features (2001) IEEE International Conference on Computer Vision and, Pattern Recognition (CVPR'01), pp. 511-518Walther, G.R., Plants in a warmer world (2004) Perspectives in Plant Ecology Evolution and Systematics, 6, pp. 169-185Walther, G.R., Post, E., Convey, P., Menzel, A., Parmesan, C., Beebee, T.J.C., Fromentin, J.M., Bairlein, F., Ecological responses to recent climate change (2002) Nature, 416, pp. 389-395White, M.A., Running, S.W., Thornton, P.E., The impact of growing-season length variability on carbon assimilation and evapotranspiration over 88years in the eastern us deciduous forest (1999) International Journal of Biometeorology, 42, pp. 139-14
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Multimedia Multimodal Geocoding
No full textThis work is developed in the context of the placing task of the MediaEval 2011 initiative. The objective is to geocode (or geotag) a set of videos, i.e., automatically assign geographical coordinates to them. This paper presents an architecture for multimodal geocoding that exploits both visual and textual descriptions associated with videos. This work also describes our efforts regarding the implementation of this architecture to demonstrate its applicability. Conducted experiments show how our multimodal approach enhances the results compared to relying on a single modality. © 2012 Authors.474477Google,Esri,Microsoft,Nokia,NVIDIAAlmeida, J., Leite, N.J., Torres, R.D.S., Comparison of video sequences with histograms of motion patterns (2011) ICIP, pp. 3673-3676Candeias, R., Martins, B., Associating relevant photos to georeferenced textual documents through rank aggregation Int. Semantic Web Conf. - Terra Cognita Workshop, 2011Choi, J., Lei, H., Friedland, G., The 2011 ICSI video location estimation system (2011) Working Notes Proc. MediaEval Workshop, 807Croft, W.B., Combining approaches to information retrieval (2002) Adv. in Inf. Retrieval, 7, pp. 1-36. , Springer USFaria, F.A., Veloso, A., De Almeida, H.M., Valle, E., Torres, R.D.S., Gonçalves, M.A., M Jr., W., Learning to rank for content-based image retrieval (2010) ACM MIR, pp. 285-294Friendly, M., Corrgrams: Exploratory displays for correlation matrices (2002) The American Statistician, 56 (4), pp. 316-324Hays, J., Efros, A.A., im2gps: Estimating geographic information from a single image (2008) CVPRJones, C.B., Purves, R.S., Geographical information retrieval (2008) Int. J. Geo. Info. Science, 22 (3), pp. 219-228Kalantidis, Y., Tolias, G., Avrithis, Y., Phinikettos, M., Spyrou, E., Mylonas, P., Kollias, S., Viral: Visual image retrieval and localization (2011) Mult. Tools and App., 51, pp. 555-592Kelm, P., Schmiedeke, S., Sikora, T., A hierarchical, multi-modal approach for placing videos on the map using millions of flickr photographs (2011) Workshop on Social and Behavioural Networked Media Access, pp. 15-20Larson, M., Soleymani, M., Serdyukov, P., Rudinac, S., Wartena, C., Murdock, V., Friedland, G., Jones, G.J.F., Automatic tagging and geotagging in video collections and communities (2011) ICMR, pp. 51:1-51:8Li, L.T., Almeida, J., Torres, R.D.S., RECOD working notes for placing task MediaEval 2011 (2011) Working Notes Proc. MediaEval Workshop, 807Luo, J., Joshi, D., Yu, J., Gallagher, A., Geotagging in multimedia and computer vision - A survey (2011) Mult. Tools and App., 51, pp. 187-211Manning, C.D., Raghavan, P., Schtze, H., (2008) Introduction to Information Retrieval, , Cambridge University Press, New York, NY, USAPedronette, D.C.G., Torres, R.D.S., Exploiting clustering approaches for image re-ranking (2011) J. Vis. Lang. and Comp., 22 (6), pp. 453-466Pedronette, D.C.G., Torres, R.D.S., Calumby, R.T., Using contextual spaces for image re-ranking and rank aggregation (2012) Mult. Tools and App., pp. 1-28Penatti, O.A.B., Li, L.T., Almeida, J., Torres, R.D.S., A Visual Approach for Video Geocoding using Bag-of-Scenes (2012) ICMRRae, A., Murdock, V., Serdyukov, P., Kelm, P., Working notes for the placing task at MediaEval 2011 (2011) Working Notes Proc. MediaEval Workshop, 807Van Laere, O., Schockaert, S., Dhoedt, B., Finding locations of flickr resources using language models and similarity search (2011) International Conference on Multimedia Retrieval, pp. 48:1-48:
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
- …
