Relevant bibliographies by topics / Retrieval models

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  2. Dissertations / Theses
  3. Books
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Academic literature on the topic 'Retrieval models'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Retrieval models"

1

Lipponen, Antti, Tero Mielonen, Mikko R. A. Pitkänen, Robert C. Levy, Virginia R. Sawyer, Sami Romakkaniemi, Ville Kolehmainen, and Antti Arola. "Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land." Atmospheric Measurement Techniques 11, no. 3 (March 19, 2018): 1529–47. http://dx.doi.org/10.5194/amt-11-1529-2018.

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Abstract. We have developed a Bayesian aerosol retrieval (BAR) algorithm for the retrieval of aerosol optical depth (AOD) over land from the Moderate Resolution Imaging Spectroradiometer (MODIS). In the BAR algorithm, we simultaneously retrieve all dark land pixels in a granule, utilize spatial correlation models for the unknown aerosol parameters, use a statistical prior model for the surface reflectance, and take into account the uncertainties due to fixed aerosol models. The retrieved parameters are total AOD at 0.55 µm, fine-mode fraction (FMF), and surface reflectances at four different wavelengths (0.47, 0.55, 0.64, and 2.1 µm). The accuracy of the new algorithm is evaluated by comparing the AOD retrievals to Aerosol Robotic Network (AERONET) AOD. The results show that the BAR significantly improves the accuracy of AOD retrievals over the operational Dark Target (DT) algorithm. A reduction of about 29 % in the AOD root mean square error and decrease of about 80 % in the median bias of AOD were found globally when the BAR was used instead of the DT algorithm. Furthermore, the fraction of AOD retrievals inside the ±(0.05+15%) expected error envelope increased from 55 to 76 %. In addition to retrieving the values of AOD, FMF, and surface reflectance, the BAR also gives pixel-level posterior uncertainty estimates for the retrieved parameters. The BAR algorithm always results in physical, non-negative AOD values, and the average computation time for a single granule was less than a minute on a modern personal computer.
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Norquist, Donald C., Paul R. Desrochers, Patrick J. McNicholl, and John R. Roadcap. "A Characterization of Cirrus Cloud Properties That Affect Laser Propagation." Journal of Applied Meteorology and Climatology 47, no. 5 (May 1, 2008): 1322–36. http://dx.doi.org/10.1175/2007jamc1756.1.

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Abstract Future high-altitude laser systems may be affected by cirrus clouds. Laser transmission models were applied to measured and retrieved cirrus properties to determine cirrus impact on power incident on a target or receiver. A major goal was to see how well radiosondes and geostationary satellite imagery could specify the required properties. Based on the use of ground-based radar and lidar measurements as a reference, errors in cirrus-top and cirrus-base height estimates from radiosonde observations were 20%–25% of geostationary satellite retrieval errors. Radiosondes had a perfect cirrus detection rate as compared with 80% for satellite detection. Ice water path and effective particle size were obtained with a published radar–lidar retrieval algorithm and a documented satellite algorithm. Radar–lidar particle size and ice water path were 1.5 and 3 times the satellite retrievals, respectively. Radar–lidar-based laser extinction coefficients were 55% greater than satellite values. Measured radar–lidar cirrus thickness was consistently greater than satellite-retrieved thickness, but radar–lidar microphysical retrieval required detection by both sensors at each range gate, which limited the retrievals’ vertical extent. Greater radar–lidar extinction and greater satellite-based cirrus thickness yielded comparable optical depths for the two independent retrievals. Laser extinction–transmission models applied to radiosonde-retrieved cirrus heights and satellite-retrieved microphysical properties revealed a significant power loss by all models as the laser beam transits the cirrus layer. This suggests that cirrus location is more important than microphysics in high-altitude laser test support. Geostationary satellite imagery may be insufficient in cirrus detection and retrieval accuracy. Humidity-sensitive radiosondes are a potential proxy for ground-based remote sensors in cirrus detection and altitude determination.
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Karthikeyan, Lanka, Ming Pan, Dasika Nagesh Kumar, and Eric F. Wood. "Effect of Structural Uncertainty in Passive Microwave Soil Moisture Retrieval Algorithm." Sensors 20, no. 4 (February 24, 2020): 1225. http://dx.doi.org/10.3390/s20041225.

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Passive microwave sensors use a radiative transfer model (RTM) to retrieve soil moisture (SM) using brightness temperatures (TB) at low microwave frequencies. Vegetation optical depth (VOD) is a key input to the RTM. Retrieval algorithms can analytically invert the RTM using dual-polarized TB measurements to retrieve the VOD and SM concurrently. Algorithms in this regard typically use the τ-ω types of models, which consist of two third-order polynomial equations and, thus, can have multiple solutions. Through this work, we find that uncertainty occurs due to the structural indeterminacy that is inherent in all τ-ω types of models in passive microwave SM retrieval algorithms. In the process, a new analytical solution for concurrent VOD and SM retrieval is presented, along with two widely used existing analytical solutions. All three solutions are applied to a fixed framework of RTM to retrieve VOD and SM on a global scale, using X-band Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB data. Results indicate that, with structural uncertainty, there ensues a noticeable impact on the VOD and SM retrievals. In an era where the sensitivity of retrieval algorithms is still being researched, we believe the structural indeterminacy of RTM identified here would contribute to uncertainty in the soil moisture retrievals.
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Field, R. D., C. Risi, G. A. Schmidt, J. Worden, A. Voulgarakis, A. N. LeGrande, A. H. Sobel, and R. J. Healy. "A Tropospheric Emission Spectrometer HDO/H<sub>2</sub>O retrieval simulator for climate models." Atmospheric Chemistry and Physics Discussions 12, no. 6 (June 5, 2012): 13827–80. http://dx.doi.org/10.5194/acpd-12-13827-2012.

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Abstract. Retrievals of the isotopic composition of water vapor from the Aura Tropospheric Emission Spectrometer (TES) have unique value in constraining moist processes in climate models. Accurate comparison between simulated and retrieved values requires that model profiles that would be poorly retrieved are excluded, and that an instrument operator be applied to the remaining profiles. Typically, this is done by sampling model output at satellite measurement points and using the quality flags and averaging kernels from individual retrievals at specific places and times. This approach is not reliable when the modeled meteorological conditions influencing retrieval sensitivity are different from those observed by the instrument at short time scales, which will be the case for free-running climate simulations. In this study, we describe an alternative, "categorical" approach to applying the instrument operator, implemented within the NASA GISS ModelE general circulation model. Retrieval quality and averaging kernel structure are predicted empirically from model conditions, rather than obtained from collocated satellite observations. This approach can be used for arbitrary model configurations, and requires no agreement between satellite-retrieved and modeled meteorology at short time scales. To test this approach, nudged simulations were conducted using both the retrieval-based and categorical operators. Cloud cover, surface temperature and free-tropospheric moisture content were the most important predictors of retrieval quality and averaging kernel structure. There was good agreement between the δD fields after applying the retrieval-based and more detailed categorical operators, with increases of up to 30‰ over the ocean and decreases of up to 40‰ over land relative to the raw model fields. The categorical operator performed better over the ocean than over land, and requires further refinement for use outside of the tropics. After applying the TES operator, ModelE had δD biases of −8‰ over ocean and −34‰ over land compared to TES δD, which were less than the biases using raw modeled δD fields.
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Field, R. D., C. Risi, G. A. Schmidt, J. Worden, A. Voulgarakis, A. N. LeGrande, A. H. Sobel, and R. J. Healy. "A Tropospheric Emission Spectrometer HDO/H<sub>2</sub>O retrieval simulator for climate models." Atmospheric Chemistry and Physics 12, no. 21 (November 12, 2012): 10485–504. http://dx.doi.org/10.5194/acp-12-10485-2012.

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Abstract. Retrievals of the isotopic composition of water vapor from the Aura Tropospheric Emission Spectrometer (TES) have unique value in constraining moist processes in climate models. Accurate comparison between simulated and retrieved values requires that model profiles that would be poorly retrieved are excluded, and that an instrument operator be applied to the remaining profiles. Typically, this is done by sampling model output at satellite measurement points and using the quality flags and averaging kernels from individual retrievals at specific places and times. This approach is not reliable when the model meteorological conditions influencing retrieval sensitivity are different from those observed by the instrument at short time scales, which will be the case for free-running climate simulations. In this study, we describe an alternative, "categorical" approach to applying the instrument operator, implemented within the NASA GISS ModelE general circulation model. Retrieval quality and averaging kernel structure are predicted empirically from model conditions, rather than obtained from collocated satellite observations. This approach can be used for arbitrary model configurations, and requires no agreement between satellite-retrieved and model meteorology at short time scales. To test this approach, nudged simulations were conducted using both the retrieval-based and categorical operators. Cloud cover, surface temperature and free-tropospheric moisture content were the most important predictors of retrieval quality and averaging kernel structure. There was good agreement between the δD fields after applying the retrieval-based and more detailed categorical operators, with increases of up to 30‰ over the ocean and decreases of up to 40‰ over land relative to the raw model fields. The categorical operator performed better over the ocean than over land, and requires further refinement for use outside of the tropics. After applying the TES operator, ModelE had δD biases of −8‰ over ocean and −34‰ over land compared to TES δD, which were less than the biases using raw model δD fields.
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van Diedenhoven, B., O. P. Hasekamp, and I. Aben. "Surface pressure retrieval from SCIAMACHY measurements in the O<sub>2</sub>A Band: validation of the measurements and sensitivity on aerosols." Atmospheric Chemistry and Physics Discussions 5, no. 2 (March 14, 2005): 1469–99. http://dx.doi.org/10.5194/acpd-5-1469-2005.

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Abstract. We perform surface pressure retrievals from cloud-free Oxygen A band measurements of SCIAMACHY. These retrievals can be well validated because surface pressure is a quantity that is, in general, accurately known from meteorological models. Therefore, surface 5 pressure retrievals and their validation provide important insight into the quality of the instrument calibration. Furthermore, they can provide insight into retrievals which are affected by similar radiation transport processes, for example the retrieval of total columns of H2O, CO, CO2 and CH4. In our retrieval aerosols are neglected. Using synthetic measurements, we show that for low to moderate or high surface albedos this 10 leads to an under- or overestimation of the retrieved surface pressures, respectively. The surface pressures retrieved from the SCIAMACHY measurements indeed show this dependence on surface albedo, when compared to the corresponding pressures from a meteorological database. However, an offset of about 30 hPa was found, which can not be caused by neglecting aerosols in the retrieval. The same offset was found 15 when comparing the retrieved surface pressures to those retrieved from co-located GOME Oxygen A band measurements. This implies a calibration error in the SCIAMACHY measurements. By adding an offset of 1% of the continuum reflectance at 756nm to the SCIAMACHY reflectance measurements, this systematic bias vanishes.
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Gong, J., and D. L. Wu. "CloudSat-constrained cloud ice water path and cloud top height retrievals from MHS 157 and 183.3 GHz radiances." Atmospheric Measurement Techniques 7, no. 6 (June 26, 2014): 1873–90. http://dx.doi.org/10.5194/amt-7-1873-2014.

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Abstract. Ice water path (IWP) and cloud top height (ht) are two of the key variables in determining cloud radiative and thermodynamical properties in climate models. Large uncertainty remains among IWP measurements from satellite sensors, in large part due to the assumptions made for cloud microphysics in these retrievals. In this study, we develop a fast algorithm to retrieve IWP from the 157, 183.3 ± 3 and 190.3 GHz radiances of the Microwave Humidity Sounder (MHS) such that the MHS cloud ice retrieval is consistent with CloudSat IWP measurements. This retrieval is obtained by constraining the empirical forward models between collocated and coincident measurements of CloudSat IWP and MHS cloud-induced radiance depression (Tcir) at these channels. The empirical forward model is represented by a look-up table (LUT) of Tcir–IWP relationships as a function of ht and the frequency channel. With ht simultaneously retrieved, the IWP is found to be more accurate. The useful range of the MHS IWP retrieval is between 0.5 and 10 kg m−2, and agrees well with CloudSat in terms of the normalized probability density function (PDF). Compared to the empirical model, current operational radiative transfer models (RTMs) still have significant uncertainties in characterizing the observed Tcir–IWP relationships. Therefore, the empirical LUT method developed here remains an effective approach to retrieving ice cloud properties from the MHS-like microwave channels.
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Gong, J., and D. L. Wu. "CloudSat-constrained cloud ice water path and cloud top height retrievals from MHS 157 and 183.3 GHz radiances." Atmospheric Measurement Techniques Discussions 6, no. 5 (September 4, 2013): 8187–233. http://dx.doi.org/10.5194/amtd-6-8187-2013.

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Abstract. Ice water path (IWP) and cloud top height (ht) are two of the key variables to determine cloud radiative and thermodynamical properties in the climate models. Large uncertainty remains among IWP measurements from satellite sensors, in large part due to the assumptions made for cloud microphysics in these retrievals. In this study, we develop a fast algorithm to retrieve IWP from the 157, 183.3 ± 3 and 190.3 GHz radiances of Microwave Humidity Sounder (MHS) such that the MHS cloud ice retrieval is consistent with CloudSat IWP measurements. This retrieval is obtained by constraining the forward models between collocated-and-coincident measurements of CloudSat IWP and MHS cloud-induced radiance depression (Tcir) at these channels. The empirical forward model is represented by a look-up-table (LUT) of Tcir–IWP relationships as a function of ht and frequency channel. With ht simultaneously retrieved, the IWP is found to be more accurate. The useful range of the MHS IWP retrieval is between 0.5 and 10 kg m−2, and agrees well with CloudSat in terms of normalized probability density function (PDF). Compared to the empirical model, current radiative transfer models (RTMs) still have significant uncertainties in characterizing the observed Tcir–IWP relationships. Therefore, the empirical LUT method developed here remains as an effective approach to retrieving ice cloud properties from the MHS-like microwave channels.
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Chimah, Jonathan N., and Friday Ibiam Ude. "Current trends in information retrieval systems: review of fuzzy set theory and fuzzy Boolean retrieval models." Journal of Library Services and Technologies 2, no. 2 (June 2020): 48–56. http://dx.doi.org/10.47524/jlst.v2i2.5.

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This paper reviews the concept and goal of Information Retrieval Systems (IRSs). It also explains the synonymous concepts in Information Retrieval (IR)which include such terms as: imprecision, vagueness, uncertainty, and inconsistency. Current trends in IRSs are discussed. Fuzzy Set Theory, Fuzzy Retrieval Modelsare reviewed. The paper also discusses extensions of Fuzzy Boolean Retrieval Models including Fuzzy techniques for documents’ indexingandFlexible query languages. Fuzzy associative mechanisms were identified to include:(1)fuzzy pseudothesauri and fuzzy ontologies which can be used to contextualize the search by expanding the set of index terms of documents;(2)an alternative use of fuzzy pseudothesarui and fuzzy ontologies is to expand the query with related terms by taking into account their varying importance of an additional termand (3)fuzzy clustering techniques, where each document can be placed within several clusters with a given strength of belonging to each cluster, can be used to expand the set of the documents retrieved in response to a query.The paper concludesby recommending that in an electronic library environment, the librarians and information scientists should acquaint themselves with these terms in order to be more equipped in helping library users retrieve online documents relevant to their information needs.
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van Diedenhoven, B., O. P. Hasekamp, and I. Aben. "Surface pressure retrieval from SCIAMACHY measurements in the O<sub>2</sub> A Band: validation of the measurements and sensitivity on aerosols." Atmospheric Chemistry and Physics 5, no. 8 (August 11, 2005): 2109–20. http://dx.doi.org/10.5194/acp-5-2109-2005.

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Abstract. We perform surface pressure retrievals from cloud-free Oxygen A band measurements of SCIAMACHY. These retrievals can be well validated because surface pressure is a quantity that is, in general, accurately known from meteorological models. Therefore, surface pressure retrievals and their validation provide important insight into the quality of the instrument calibration. Furthermore, they can provide insight into retrievals which are affected by similar radiation transport processes, for example the retrieval of total columns of H2O, CO, CO2 and CH4. In our retrieval aerosols are neglected. Using synthetic measurements, it is shown that for low to moderate surface albedos this leads to an underestimation of the retrieved surface pressures. For high surface albedos this generally leads to an overestimation of the retrieved surface pressures. The surface pressures retrieved from the SCIAMACHY measurements indeed show this dependence on surface albedo, when compared to the corresponding pressures from a meteorological database. However, an offset of about 20 hPa was found, which can not be caused by neglecting aerosols in the retrieval. The same offset was found when comparing the retrieved surface pressures to those retrieved from co-located GOME Oxygen A band measurements. This implies a calibration error in the SCIAMACHY measurements. By adding an offset of 0.86% of the continuum reflectance at 756 nm to the SCIAMACHY reflectance measurements, this systematic bias vanishes.
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Dissertations / Theses on the topic "Retrieval models"

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Stathopoulos, Vassilios. "Generative probabilistic models for image retrieval." Thesis, University of Glasgow, 2012. http://theses.gla.ac.uk/3360/.

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Searching for information is a recurring problem that almost everyone has faced at some point. Being in a library looking for a book, searching through newspapers and magazines for an old article or searching through emails for an old conversation with a colleague are some examples of the searching activity. These are some of the many situations where someone; the “user”; has some vague idea of the information he is looking for; an “information need”; and is searching through a large number of documents, emails or articles; “information items”; to find the most “relevant” item for his purpose. In this thesis we study the problem of retrieving images from large image archives. We consider two different approaches for image retrieval. The first approach is content based image retrieval where the user is searching images using a query image. The second approach is semantic retrieval where the users expresses his query using keywords. We proposed a unified framework to treat both approaches using generative probabilistic models in order to rank and classify images with respect to user queries. The methodology presented in this Thesis is evaluated on a real image collection and compared against state of the art methods.
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Morgan, Richard. "Component library retrieval using property models." Thesis, Durham University, 1991. http://etheses.dur.ac.uk/6095/.

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The re-use of products such as code, specifications, design decisions and documentation has been proposed as a method for increasing software productivity and reliability. A major problem that has still to be adequately solved is the storage and retrieval of re-usable 'components'. Current methods, such as keyword retrieval and catalogues, rely on the use of names to describe components or categories. This is inadequate for all but a few well established components and categories; in the majority of cases names do not convey sufficient information on which to base a decision to retrieve. One approach to this problem is to describe components using a formal specification. However this is impractical for two reasons; firstly, the limitations of theorem proving would severely restrict the complexity of components that could be retrieved and secondly the retrieval mechanism would need to have a method of retrieving components with 'similar' specifications. This thesis proposes the use of formal 'property' models to represent the key functionality of components. Retrieval of components can then take place on the basis of a property model produced by the library's users. These models only describe the key properties of a component, thereby making the task of comparing properties feasible. Views are introduced as a method of relating similar, non identical property models, and the use of these views facilitates the re-use of components with similar properties. The language Miramod has been developed for the purpose of describing components, and a Miramod compiler and property prover which allow Miramod models to be compared for similarity, have been designed and implemented. These tools have indicated that model based component library retrieval is feasible at relatively low levels of the programming process, and future work is suggested to extend the method to encompass earlier stages in the development of large systems.
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Vasconcelos, Nuno Miguel Borges de Pinho Cruz de. "Bayesian models for visual information retrieval." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/62947.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2000.<br>Includes bibliographical references (leaves 192-208).<br>This thesis presents a unified solution to visual recognition and learning in the context of visual information retrieval. Realizing that the design of an effective recognition architecture requires careful consideration of the interplay between feature selection, feature representation, and similarity function, we start by searching for a performance criteria that can simultaneously guide the design of all three components. A natural solution is to formulate visual recognition as a decision theoretical problem, where the goal is to minimize the probability of retrieval error. This leads to a Bayesian architecture that is shown to generalize a significant number of previous recognition approaches, solving some of the most challenging problems faced by these: joint modeling of color and texture, objective guidelines for controlling the trade-off between feature transformation and feature representation, and unified support for local and global queries without requiring image segmentation. The new architecture is shown to perform well on color, texture, and generic image databases, providing a good trade-off between retrieval accuracy, invariance, perceptual relevance of similarity judgments, and complexity. Because all that is needed to perform optimal Bayesian decisions is the ability to evaluate beliefs on the different hypothesis under consideration, a Bayesian architecture is not restricted to visual recognition. On the contrary, it establishes a universal recognition language (the language of probabilities) that provides a computational basis for the integration of information from multiple content sources and modalities. In result, it becomes possible to build retrieval systems that can simultaneously account for text, audio, video, or any other content modalities. Since the ability to learn follows from the ability to integrate information over time, this language is also conducive to the design of learning algorithms. We show that learning is, indeed, an important asset for visual information retrieval by designing both short and long-term learning mechanisms. Over short time scales (within a retrieval session), learning is shown to assure faster convergence to the desired target images. Over long time scales (between retrieval sessions), it allows the retrieval system to tailor itself to the preferences of particular users. In both cases, all the necessary computations are carried out through Bayesian belief propagation algorithms that, although optimal in a decision-theoretic sense, are extremely simple, intuitive, and easy to implement.<br>by Nuno Miguel Borges de Pinho Cruz de Vasconcelos.<br>Ph.D.
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Mensink, Thomas. "Learning Image Classification and Retrieval Models." Thesis, Grenoble, 2012. http://www.theses.fr/2012GRENM113/document.

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Nous assistons actuellement à une explosion de la quantité des données visuelles. Par exemple, plusieurs millions de photos sont partagées quotidiennement sur les réseaux sociaux. Les méthodes d'interprétation d'images vise à faciliter l'accès à ces données visuelles, d'une manière sémantiquement compréhensible. Dans ce manuscrit, nous définissons certains buts détaillés qui sont intéressants pour les taches d'interprétation d'images, telles que la classification ou la recherche d'images, que nous considérons dans les trois chapitres principaux. Tout d'abord, nous visons l'exploitation de la nature multimodale de nombreuses bases de données, pour lesquelles les documents sont composés d'images et de descriptions textuelles. Dans ce but, nous définissons des similarités entre le contenu visuel d'un document, et la description textuelle d'un autre document. Ces similarités sont calculées en deux étapes, tout d'abord nous trouvons les voisins visuellement similaires dans la base multimodale, puis nous utilisons les descriptions textuelles de ces voisins afin de définir une similarité avec la description textuelle de n'importe quel document. Ensuite, nous présentons une série de modèles structurés pour la classification d'images, qui encodent explicitement les interactions binaires entre les étiquettes (ou labels). Ces modèles sont plus expressifs que des prédicateurs d'étiquette indépendants, et aboutissent à des prédictions plus fiables, en particulier dans un scenario de prédiction interactive, où les utilisateurs fournissent les valeurs de certaines des étiquettes d'images. Un scenario interactif comme celui-ci offre un compromis intéressant entre la précision, et l'effort d'annotation manuelle requis. Nous explorons les modèles structurés pour la classification multi-étiquette d'images, pour la classification d'image basée sur les attributs, et pour l'optimisation de certaines mesures de rang spécifiques. Enfin, nous explorons les classifieurs par k plus proches voisins, et les classifieurs par plus proche moyenne, pour la classification d'images à grande échelle. Nous proposons des méthodes d'apprentissage de métrique efficaces pour améliorer les performances de classification, et appliquons ces méthodes à une base de plus d'un million d'images d'apprentissage, et d'un millier de classes. Comme les deux méthodes de classification permettent d'incorporer des classes non vues pendant l'apprentissage à un coût presque nul, nous avons également étudié leur performance pour la généralisation. Nous montrons que la classification par plus proche moyenne généralise à partir d'un millier de classes, sur dix mille classes à un coût négligeable, et les performances obtenus sont comparables à l'état de l'art<br>We are currently experiencing an exceptional growth of visual data, for example, millions of photos are shared daily on social-networks. Image understanding methods aim to facilitate access to this visual data in a semantically meaningful manner. In this dissertation, we define several detailed goals which are of interest for the image understanding tasks of image classification and retrieval, which we address in three main chapters. First, we aim to exploit the multi-modal nature of many databases, wherein documents consists of images with a form of textual description. In order to do so we define similarities between the visual content of one document and the textual description of another document. These similarities are computed in two steps, first we find the visually similar neighbors in the multi-modal database, and then use the textual descriptions of these neighbors to define a similarity to the textual description of any document. Second, we introduce a series of structured image classification models, which explicitly encode pairwise label interactions. These models are more expressive than independent label predictors, and lead to more accurate predictions. Especially in an interactive prediction scenario where a user provides the value of some of the image labels. Such an interactive scenario offers an interesting trade-off between accuracy and manual labeling effort. We explore structured models for multi-label image classification, for attribute-based image classification, and for optimizing for specific ranking measures. Finally, we explore k-nearest neighbors and nearest-class mean classifiers for large-scale image classification. We propose efficient metric learning methods to improve classification performance, and use these methods to learn on a data set of more than one million training images from one thousand classes. Since both classification methods allow for the incorporation of classes not seen during training at near-zero cost, we study their generalization performances. We show that the nearest-class mean classification method can generalize from one thousand to ten thousand classes at negligible cost, and still perform competitively with the state-of-the-art
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Rebai, Ahmed. "Interactive Object Retrieval using Interpretable Visual Models." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00608467.

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This thesis is an attempt to improve visual object retrieval by allowing users to interact with the system. Our solution lies in constructing an interactive system that allows users to define their own visual concept from a concise set of visual patches given as input. These patches, which represent the most informative clues of a given visual category, are trained beforehand with a supervised learning algorithm in a discriminative manner. Then, and in order to specialize their models, users have the possibility to send their feedback on the model itself by choosing and weighting the patches they are confident of. The real challenge consists in how to generate concise and visually interpretable models. Our contribution relies on two points. First, in contrast to the state-of-the-art approaches that use bag-of-words, we propose embedding local visual features without any quantization, which means that each component of the high-dimensional feature vectors used to describe an image is associated to a unique and precisely localized image patch. Second, we suggest using regularization constraints in the loss function of our classifier to favor sparsity in the models produced. Sparsity is indeed preferable for concision (a reduced number of patches in the model) as well as for decreasing prediction time. To meet these objectives, we developed a multiple-instance learning scheme using a modified version of the BLasso algorithm. BLasso is a boosting-like procedure that behaves in the same way as Lasso (Least Absolute Shrinkage and Selection Operator). It efficiently regularizes the loss function with an additive L1-constraint by alternating between forward and backward steps at each iteration. The method we propose here is generic in the sense that it can be used with any local features or feature sets representing the content of an image region.
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Pérez-Sancho, Carlos. "Stochastic language models for music information retrieval." Doctoral thesis, Universidad de Alicante, 2009. http://hdl.handle.net/10045/14217.

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Tam, Kwok Leung. "Indexing and retrieval of 3D articulated geometry models." Thesis, Durham University, 2009. http://etheses.dur.ac.uk/21/.

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In this PhD research study, we focus on building a content-based search engine for 3D articulated geometry models. 3D models are essential components in nowadays graphic applications, and are widely used in the game, animation and movies production industry. With the increasing number of these models, a search engine not only provides an entrance to explore such a huge dataset, it also facilitates sharing and reusing among different users. In general, it reduces production costs and time to develop these 3D models. Though a lot of retrieval systems have been proposed in recent years, search engines for 3D articulated geometry models are still in their infancies. Among all the works that we have surveyed, reliability and efficiency are the two main issues that hinder the popularity of such systems. In this research, we have focused our attention mainly to address these two issues. We have discovered that most existing works design features and matching algorithms in order to reflect the intrinsic properties of these 3D models. For instance, to handle 3D articulated geometry models, it is common to extract skeletons and use graph matching algorithms to compute the similarity. However, since this kind of feature representation is complex, it leads to high complexity of the matching algorithms. As an example, sub-graph isomorphism can be NP-hard for model graph matching. Our solution is based on the understanding that skeletal matching seeks correspondences between the two comparing models. If we can define descriptive features, the correspondence problem can be solved by bag-based matching where fast algorithms are available. In the first part of the research, we propose a feature extraction algorithm to extract such descriptive features. We then convert the skeletal matching problems into bag-based matching. We further define metric similarity measure so as to support fast search. We demonstrate the advantages of this idea in our experiments. The improvement on precision is 12\% better at high recall. The indexing search of 3D model is 24 times faster than the state of the art if only the first relevant result is returned. However, improving the quality of descriptive features pays the price of high dimensionality. Curse of dimensionality is a notorious problem on large multimedia databases. The computation time scales exponentially as the dimension increases, and indexing techniques may not be useful in such situation. In the second part of the research, we focus ourselves on developing an embedding retrieval framework to solve the high dimensionality problem. We first argue that our proposed matching method projects 3D models on manifolds. We then use manifold learning technique to reduce dimensionality and maximize intra-class distances. We further propose a numerical method to sub-sample and fast search databases. To preserve retrieval accuracy using fewer landmark objects, we propose an alignment method which is also beneficial to existing works for fast search. The advantages of the retrieval framework are demonstrated in our experiments that it alleviates the problem of curse of dimensionality. It also improves the efficiency (3.4 times faster) and accuracy (30\% more accurate) of our matching algorithm proposed above. In the third part of the research, we also study a closely related area, 3D motions. 3D motions are captured by sticking sensor on human beings. These captured data are real human motions that are used to animate 3D articulated geometry models. Creating realistic 3D motions is an expensive and tedious task. Although 3D motions are very different from 3D articulated geometry models, we observe that existing works also suffer from the problem of temporal structure matching. This also leads to low efficiency in the matching algorithms. We apply the same idea of bag-based matching into the work of 3D motions. From our experiments, the proposed method has a 13\% improvement on precision at high recall and is 12 times faster than existing works. As a summary, we have developed algorithms for 3D articulated geometry models and 3D motions, covering feature extraction, feature matching, indexing and fast search methods. Through various experiments, our idea of converting restricted matching to bag-based matching improves matching efficiency and reliability. These have been shown in both 3D articulated geometry models and 3D motions. We have also connected 3D matching to the area of manifold learning. The embedding retrieval framework not only improves efficiency and accuracy, but has also opened a new area of research.
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Mjali, Siyabonga Zimozoxolo. "Latent semantic models : a study of probabilistic models for text in information retrieval." Diss., University of Pretoria, 2020. http://hdl.handle.net/2263/73881.

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Large volumes of text is being generated every minute which necessitates effective and robust tools to retrieve relevant information. Supervised learning approaches have been explored extensively for this task, but it is difficult to secure large collections of labelled data to train this set of models. Since a supervised approach is too expensive in terms of annotating data, we consider unsupervised methods such as topic models and word embeddings in order to represent corpora in lower dimensional semantic spaces. Furthermore, we investigate different distance measures to capture similarity between indexed documents based on their semantic distributions. These include cosine, soft cosine and Jensen-Shannon similarities. This collection of methods discussed in this work allows for the unsupervised association of semantic similar texts which has a wide range of applications such as fake news detection, sociolinguistics and sentiment analysis.<br>Mini Dissertation (MSc)--University of Pretoria, 2020.<br>The Hub Internship<br>Centre for Artificial Intelligence Research<br>Statistics<br>MSc (Mathematical Statistics)<br>Unrestricted
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Belkacem, Thiziri. "Neural models for information retrieval : towards asymmetry sensitive approaches based on attention models." Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30167.

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Ce travail se situe dans le contexte de la recherche d'information (RI) utilisant des techniques d'intelligence artificielle (IA) telles que l'apprentissage profond (DL). Il s'intéresse à des tâches nécessitant l'appariement de textes, telles que la recherche ad-hoc, le domaine du questions-réponses et l'identification des paraphrases. L'objectif de cette thèse est de proposer de nouveaux modèles, utilisant les méthodes de DL, pour construire des modèles d'appariement basés sur la sémantique de textes, et permettant de pallier les problèmes de l'inadéquation du vocabulaire relatifs aux représentations par sac de mots, ou bag of words (BoW), utilisées dans les modèles classiques de RI. En effet, les méthodes classiques de comparaison de textes sont basées sur la représentation BoW qui considère un texte donné comme un ensemble de mots indépendants. Le processus d'appariement de deux séquences de texte repose sur l'appariement exact entre les mots. La principale limite de cette approche est l'inadéquation du vocabulaire. Ce problème apparaît lorsque les séquences de texte à apparier n'utilisent pas le même vocabulaire, même si leurs sujets sont liés. Par exemple, la requête peut contenir plusieurs mots qui ne sont pas nécessairement utilisés dans les documents de la collection, notamment dans les documents pertinents. Les représentations BoW ignorent plusieurs aspects, tels que la structure du texte et le contexte des mots. Ces caractéristiques sont très importantes et permettent de différencier deux textes utilisant les mêmes mots et dont les informations exprimées sont différentes. Un autre problème dans l'appariement de texte est lié à la longueur des documents. Les parties pertinentes peuvent être réparties de manières différentes dans les documents d'une collection. Ceci est d'autant vrai dans les documents volumineux qui ont tendance à couvrir un grand nombre de sujets et à inclure un vocabulaire variable. Un document long pourrait ainsi comporter plusieurs passages pertinents qu'un modèle d'appariement doit capturer. Contrairement aux documents longs, les documents courts sont susceptibles de concerner un sujet spécifique et ont tendance à contenir un vocabulaire plus restreint. L'évaluation de leur pertinence est en principe plus simple que celle des documents plus longs. Dans cette thèse, nous avons proposé différentes contributions répondant chacune à l'un des problèmes susmentionnés. Tout d'abord, afin de résoudre le problème d'inadéquation du vocabulaire, nous avons utilisé des représentations distribuées des mots (plongement lexical) pour permettre un appariement basé sur la sémantique entre les différents mots. Ces représentations ont été utilisées dans des applications de RI où la similarité document-requête est calculée en comparant tous les vecteurs de termes de la requête avec tous les vecteurs de termes du document, indifféremment. Contrairement aux modèles proposés dans l'état-de-l'art, nous avons étudié l'impact des termes de la requête concernant leur présence/absence dans un document. Nous avons adopté différentes stratégies d'appariement document/requête. L'intuition est que l'absence des termes de la requête dans les documents pertinents est en soi un aspect utile à prendre en compte dans le processus de comparaison. En effet, ces termes n'apparaissent pas dans les documents de la collection pour deux raisons possibles : soit leurs synonymes ont été utilisés ; soit ils ne font pas partie du contexte des documents en questions<br>This work is situated in the context of information retrieval (IR) using machine learning (ML) and deep learning (DL) techniques. It concerns different tasks requiring text matching, such as ad-hoc research, question answering and paraphrase identification. The objective of this thesis is to propose new approaches, using DL methods, to construct semantic-based models for text matching, and to overcome the problems of vocabulary mismatch related to the classical bag of word (BoW) representations used in traditional IR models. Indeed, traditional text matching methods are based on the BoW representation, which considers a given text as a set of independent words. The process of matching two sequences of text is based on the exact matching between words. The main limitation of this approach is related to the vocabulary mismatch. This problem occurs when the text sequences to be matched do not use the same vocabulary, even if their subjects are related. For example, the query may contain several words that are not necessarily used in the documents of the collection, including relevant documents. BoW representations ignore several aspects about a text sequence, such as the structure the context of words. These characteristics are important and make it possible to differentiate between two texts that use the same words but expressing different information. Another problem in text matching is related to the length of documents. The relevant parts can be distributed in different ways in the documents of a collection. This is especially true in large documents that tend to cover a large number of topics and include variable vocabulary. A long document could thus contain several relevant passages that a matching model must capture. Unlike long documents, short documents are likely to be relevant to a specific subject and tend to contain a more restricted vocabulary. Assessing their relevance is in principle simpler than assessing the one of longer documents. In this thesis, we have proposed different contributions, each addressing one of the above-mentioned issues. First, in order to solve the problem of vocabulary mismatch, we used distributed representations of words (word embedding) to allow a semantic matching between the different words. These representations have been used in IR applications where document/query similarity is computed by comparing all the term vectors of the query with all the term vectors of the document, regardless. Unlike the models proposed in the state-of-the-art, we studied the impact of query terms regarding their presence/absence in a document. We have adopted different document/query matching strategies. The intuition is that the absence of the query terms in the relevant documents is in itself a useful aspect to be taken into account in the matching process. Indeed, these terms do not appear in documents of the collection for two possible reasons: either their synonyms have been used or they are not part of the context of the considered documents. The methods we have proposed make it possible, on the one hand, to perform an inaccurate matching between the document and the query, and on the other hand, to evaluate the impact of the different terms of a query in the matching process. Although the use of word embedding allows semantic-based matching between different text sequences, these representations combined with classical matching models still consider the text as a list of independent elements (bag of vectors instead of bag of words). However, the structure of the text as well as the order of the words is important. Any change in the structure of the text and/or the order of words alters the information expressed. In order to solve this problem, neural models were used in text matching
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Wessel, Raoul [Verfasser]. "Shape Retrieval Methods for Architectural 3D Models / Raoul Wessel." Bonn : Universitäts- und Landesbibliothek Bonn, 2014. http://d-nb.info/1048091503/34.

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Books on the topic "Retrieval models"

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Losee, Robert M. Text retrieval and filtering: Analytic models of performance. Dordrecht: Springer, 1998.

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Text retrieval and filtering: Analytic models of performance. Boston: Kluwer Academic Publishers, 1998.

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Crestani, Fabio. Information Retrieval: Uncertainty and Logics: Advanced Models for the Representation and Retrieval of Information. Boston, MA: Springer US, 1998.

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Aerts, Diederik, Andrei Khrennikov, Massimo Melucci, and Bourama Toni, eds. Quantum-Like Models for Information Retrieval and Decision-Making. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25913-6.

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Benois-Pineau, Jenny, and Patrick Le Callet, eds. Visual Content Indexing and Retrieval with Psycho-Visual Models. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57687-9.

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Next generation search engines: Advanced models for information retrieval. Hershey PA: Information Science Reference, 2012.

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Rambaud, Salvador Cruz. Algebraic models for accounting systems. Singapore: World Scientific, 2010.

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Qiu, Liwen. Probabilistic models of search state and path patterns in hypertext information retrieval systems. Ottawa: National Library of Canada, 1991.

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Paquette, Gilbert. Visual knowledge modeling for semantic web technologies: Models and ontologies. Hershey, PA: Information Science Reference, 2010.

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Lester, Neil. Assisting the software reuse process through classification and retrieval of software models. [s.l: The Author], 2000.

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Book chapters on the topic "Retrieval models"

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Stein, Benno, Tim Gollub, and Maik Anderka. "Retrieval Models." In Encyclopedia of Social Network Analysis and Mining, 1583–86. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_117.

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Stein, Benno, Tim Gollub, and Maik Anderka. "Retrieval Models." In Encyclopedia of Social Network Analysis and Mining, 2251–56. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_117.

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Stein, Benno, Tim Gollub, and Maik Anderka. "Retrieval Models." In Encyclopedia of Social Network Analysis and Mining, 1–7. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_117-1.

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Westerveld, Thijs, Arjen de Vries, and Franciska de Jong. "Generative Probabilistic Models." In Multimedia Retrieval, 177–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72895-5_6.

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Ceri, Stefano, Alessandro Bozzon, Marco Brambilla, Emanuele Della Valle, Piero Fraternali, and Silvia Quarteroni. "Information Retrieval Models." In Web Information Retrieval, 27–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39314-3_3.

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Amati, Giambattista. "Information Retrieval Models." In Encyclopedia of Database Systems, 1976–81. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_916.

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Amati, Giambattista. "Information Retrieval Models." In Encyclopedia of Database Systems, 1523–28. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_916.

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Amati, Giambattista. "Information Retrieval Models." In Encyclopedia of Database Systems, 1–7. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_916-2.

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Metzler, Donald. "Classical Retrieval Models." In A Feature-Centric View of Information Retrieval, 7–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22898-8_2.

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Dominich, Sándor. "Information Retrieval Models." In Mathematical Modelling: Theory and Applications, 95–159. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0752-8_3.

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Conference papers on the topic "Retrieval models"

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Li, Min, J. Y. H. Fuh, Y. F. Zhang, and Z. M. Qiu. "General and Partial Shape Matching Approaches on Feature-Based CAD Models to Support Efficient Part Retrieval." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49159.

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Presently, mechanical design activities are almost fully digitalized and a large percentage of CAD models that are digitally created can be reused to facilitate new designs. Conventional alphanumeric-based part retrieval methods are error-prone. In order to support users to efficiently locate desirable parts to reuse, two novel knowledge-based approaches for feature-based model retrieval are proposed in the paper, namely general shape matching and partial shape matching, to assess CAD models by measuring their overall similarity without details and local similarity of sub-parts, respectively. The first approach simplifies feature-based CAD models from fully detailed to less detailed, and progressively simplified shapes are characterized by general shape descriptors. When a 3D query model that represents the general shape of a desired part is sketched, the approach compares the query model against pre-generated general shape descriptors so that all parts similar to the 3D query in overall shapes are retrieved; therefore the general shape matching is achieved. The second approach extracts reusable sub-parts from feature-based CAD models in an unsupervised way and indexes them using partial shape descriptors. Based on the partial shape descriptors indexed, designers could retrieve all existing parts sharing a desirable sub-part by sketching the shape of the sub-part; the partial shape matching is thereby realized to facilitate the part retrieval on local similarity. The approaches proposed in this paper are implemented on a prototype system and tested by hundreds of mechanical parts. The preliminary results show that the proposed approaches are feasible and promising.
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Horstmann, Jochen, Wolfgang Koch, and Susanne Lehner. "High Resolution Wind Fields Retrieved From Spaceborne Synthetic Aperture Radar Images in Comparison to Numerical Models." In ASME 2002 21st International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2002. http://dx.doi.org/10.1115/omae2002-28301.

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This paper introduces a recently developed algorithm to retrieve high-resolution wind fields over the ocean surface from spaceborne synthetic aperture radar (SAR) data. The algorithm consists of two parts, the first for determining wind direction and the second for wind speed retrieval. Wind directions are extracted from wind induced streaks e.g. from boundary layer rolls, Langmuir cells, or wind shadowing, which are approximately in line with the mean wind direction. Wind speed is derived from the normalized radar cross section (NRCS) and image geometry of the SAR image, together with the local retrieved wind direction. The application of SAR-wind retrieval in coastal regions is demonstrated using data acquired aboard the European satellites ERS-1 and ERS-2 and the Canadian satellite RADARSAT-1. These data allow to measure wind fields of an area of up to 500 km × 500 km with a resolution of up to 200 m. To improve and validate the set-up of numerical high-resolution models in coastal regions SAR-retrieved wind fields offer an unique opportunity. This is shown by comparisons of wind fields measured by SAR to results of the numerical model REMO, HIRLAM and GESIMA.
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de Rijke, Maarten. "Session details: Retrieval Models." In SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/3252665.

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Ujjwal, Dasu, Prakhar Rastogi, and Siril Siddhartha. "Analysis of retrieval models for cross language information retrieval." In 2016 10th International Conference on Intelligent Systems and Control (ISCO). IEEE, 2016. http://dx.doi.org/10.1109/isco.2016.7727028.

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Udoyen, Nsikan, and David W. Rosen. "Description Logic Representation of Finite Element Analysis Models for Automated Retrieval." In ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/detc2006-99451.

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Adaptive reuse of archived parametric finite element analysis (FEA) models to model similar, new problems is an important part of the process of learning to apply the finite element method to specific domains. Retrieval of relevant archived models and supporting documents from electronic repositories is difficult when a modeler is unable to describe information needs precisely in a query using keywords. The use of description logic concepts to describe archived models and build expandable classification hierarchies to facilitate retrieval is proposed and illustrated. A domain-independent retrieval algorithm based on the traversal of description logic concept hierarchies is introduced. Its usefulness is asserted by: • showing that intent behind FEA models can be represented precisely using a fairly inexpressive description logic; • showing that standard description logic inference supports retrieval of FEA models based on a modelers’ intent.
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Nallapati, Ramesh. "Discriminative models for information retrieval." In the 27th annual international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1008992.1009006.

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Choi, Jaeho, W. Bruce Croft, and Jin Young Kim. "Quality models for microblog retrieval." In the 21st ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2396761.2398527.

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Lipka, Nedim, and Benno Stein. "Robust Models in Information Retrieval." In 2011 22nd International Conference on Database and Expert Systems Applications (DEXA). IEEE, 2011. http://dx.doi.org/10.1109/dexa.2011.73.

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Bollmann, P., and S. K. M. Wong. "Adaptive linear information retrieval models." In the 10th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 1987. http://dx.doi.org/10.1145/42005.42023.

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Cortelazzo, G. M., and N. Orio. "Retrieval of Colored 3D Models." In Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06). IEEE, 2006. http://dx.doi.org/10.1109/3dpvt.2006.121.

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Reports on the topic "Retrieval models"

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Conser, Erik. Improved Scoring Models for Semantic Image Retrieval Using Scene Graphs. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.5767.

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McFarquhar, Greg. Determining Best Estimates and Uncertainties in Cloud Microphysical Parameters from ARM Field Data: Implications for Models, Retrieval Schemes and Aerosol-Cloud-Radiation Interactions. Office of Scientific and Technical Information (OSTI), December 2015. http://dx.doi.org/10.2172/1233352.

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Ng, Kenney. A Maximum Likelihood Ratio Information Retrieval Model. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada456243.

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Fordham, R. A. Tank waste remediation system simulation analysis retrieval model. Office of Scientific and Technical Information (OSTI), September 1996. http://dx.doi.org/10.2172/327599.

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Forbus, Kenneth D., Dedre Gentner, and Keith Law. MAC/FAC: A Model of Similarity-Based Retrieval. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada286291.

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Forbus, Kenneth D., Dedre Gentner, and Keith Law. MAC/FAC: A Model of Similarity-Based Retrieval. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada288515.

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Clark, H. The Opstat Client-Server Model for Statistics Retrieval. RFC Editor, September 1995. http://dx.doi.org/10.17487/rfc1856.

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Schillings, P. L., D. L. Heiser, and S. P. Fogdall. Buried waste program retrieval process conceptual model design. Office of Scientific and Technical Information (OSTI), December 1988. http://dx.doi.org/10.2172/6329901.

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Heiser, D. L., and S. P. Fogdall. Buried waste program retrieval process model: Revision 1. Office of Scientific and Technical Information (OSTI), March 1989. http://dx.doi.org/10.2172/6348672.

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Vachon, P. W., and F. W. Dobson. Wind Retrieval from RADARSAT SAR Images: Selection of a Suitable C-band HH Polarization Wind Retrieval Model. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2000. http://dx.doi.org/10.4095/219536.

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