Ribana Roscher and Wolfgang Förstner and Björn Waske, "I2VM: Incremental import vector machines", Image and Vision Computing., May, 2012. Vol. 30(4-5), pp. 263-278. 2012.

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We introduce an innovative incremental learner called incremental import vector machines ((IVM)-V-2). The kernel-based discriminative approach is able to deal with complex data distributions. Additionally, the learner is sparse for an efficient training and testing and has a probabilistic output. We particularly investigate the reconstructive component of import vector machines, in order to use it for robust incremental teaming. By performing incremental update steps, we are able to add and remove data samples, as well as update the current set of model parameters for incremental learning. By using various standard benchmarks, we demonstrate how (IVM)-V-2 is competitive or superior to other incremental methods. It is also shown that our approach is capable of managing concept-drifts in the data distributions. (C) 2012 Elsevier B.V. All rights reserved.

@article{Roscher2012I2VM,
  author = {Roscher, Ribana and F\"orstner, Wolfgang and Waske, Bj\"orn},
  title = {I2VM: Incremental import vector machines},
  journal = {Image and Vision Computing},
  year = {2012},
  volume = {30},
  number = {4-5},
  pages = {263--278},
  doi = {10.1016/j.imavis.2012.04.004}
}

Ribana Roscher and Björn Waske and Wolfgang Förstner, "Incremental Import Vector Machines for Classifying Hyperspectral Data", IEEE Transactions on Geoscience and Remote Sensing., September, 2012. Vol. 50(9), pp. 3463-3473. 2012.

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In this paper, we propose an incremental learning strategy for import vector machines (IVM), which is a sparse kernel logistic regression approach. We use the procedure for the concept of self-training for sequential classification of hyperspectral data. The strategy comprises the inclusion of new training samples to increase the classification accuracy and the deletion of noninformative samples to be memory and runtime efficient. Moreover, we update the parameters in the incremental IVM model without retraining from scratch. Therefore, the incremental classifier is able to deal with large data sets. The performance of the IVM in comparison to support vector machines (SVM) is evaluated in terms of accuracy, and experiments are conducted to assess the potential of the probabilistic outputs of the IVM. Experimental results demonstrate that the IVM and SVM perform similar in terms of classification accuracy. However, the number of import vectors is significantly lower when compared to the number of support vectors, and thus, the computation time during classification can be decreased. Moreover, the probabilities provided by IVM are more reliable, when compared to the probabilistic information, derived from an SVM's output. In addition, the proposed self-training strategy can increase the classification accuracy. Overall, the IVM and its incremental version is worthwhile for the classification of hyperspectral data.

@article{Roscher2012Incremental,
  author = {Roscher, Ribana and Waske, Bj\"orn and F\"orstner, Wolfgang},
  title = {Incremental Import Vector Machines for Classifying Hyperspectral Data},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2012},
  volume = {50},
  number = {9},
  pages = {3463--3473},
  doi = {10.1109/TGRS.2012.2184292}
}

Ribana Roscher, "Sequential Learning using Incremental Import Vector Machines for Semantic Segmentation". Thesis at: Department of Photogrammetry, University of Bonn. 2012.

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We propose an innovative machine learning algorithm called incremental import vector machines that is used for classification purposes. The classifier is specifically designed for the task of sequential learning, in which the data samples are successively presented to the classifier. The motivation for our work comes from the effort to formulate a classifier that can manage the major challenges of sequential learning problems, while being a powerful classifier in terms of classification accuracy, efficiency and meaningful output. One challenge of sequential learning is that data samples are not completely available to the learner at a given point of time and generally, waiting for a representative number of data is undesirable and impractical. Thus, in order to allow for a classification of given data samples at any time, the learning phase of the classifier model needs to start immediately, even if not all training samples are available. Another challenge is that the number of sequential arriving data samples can be very large or even infinite and thus, not all samples can be stored. Furthermore, the distribution of the sample can vary over time and the classifier model needs to remain stable and unchanged to irrelevant samples while being plastic to new, important samples. Therefore our key contribution is to develop, analyze and evaluate a powerful incremental learner for sequential learning which we call incremental import vector machines (I2VMs). The classifier is based on the batch machine learning algorithm import vector machines, which was developed by Zhu and Hastie (2005). I2VM is a kernel-based, discriminative classifier and thus, is able to deal with complex data distributions. Additionally ,the learner is sparse for an efficient training and testing and has a probabilistic output. A key achievement of this thesis is the verification and analysis of the discriminative and reconstructive model components of IVM and I2VM. While discriminative classifiers try to separate the classes as well as possible, classifiers with a reconstructive component aspire to have a high information content in order to approximate the distribution of the data samples. Both properties are necessary for a powerful incremental classifier. A further key achievement is the formulation of the incremental learning strategy of I2VM. The strategy deals with adding and removing data samples and the update of the current set of model parameters. Furthermore, also new classes and features can be incorporated. The learning strategy adapts the model continuously, while keeping it stable and efficient. In our experiments we use I2VM for the semantic segmentation of images from an image database, for large area land cover classification of overlapping remote sensing images and for object tracking in image sequences. We show that I2VM results in superior or competitive classification accuracies to comparable classifiers. A substantial achievement of the thesis is that I2VM's performance is independent of the ordering of the data samples and a reconsidering of already encountered samples for learning is not necessary. A further achievement is that I2VM is able to deal with very long data streams without a loss in the efficiency. Furthermore, as another achievement, we show that I2VM provide reliable posterior probabilities since samples with high class probabilities are accurately classified, whereas relatively low class probabilities are more likely referred to misclassified samples.

@phdthesis{Roscher2012Sequential,
  author = {Roscher, Ribana},
  title = {Sequential Learning using Incremental Import Vector Machines for Semantic Segmentation},
  school = {Department of Photogrammetry, University of Bonn},
  year = {2012},
  url = {http://hss.ulb.uni-bonn.de/2012/3009/3009.htm}
}

Ribana Roscher and Björn Waske and Wolfgang Förstner, "Evaluation of Import Vector Machines for Classifying Hyperspectral Data" 2012.

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We evaluate the performance of Import Vector Machines (IVM),a sparse Kernel Logistic Regression approach, for the classification of hyperspectral data. The IVM classifier is applied on two different data sets, using different number of training samples. The performance of IVM to Support Vector Machines (SVM) is compared in terms of accuracy and sparsity. Moreover, the impact of the training sample set on the accuracy and stability of IVM was investigated. The results underline that the IVM perform similar when compared to the popular SVM in terms of accuracy. Moreover, the number of import vectors from the IVM is significantly lower when compared to the number of support vectors from the SVM. Thus, the classification process of the IVM is faster. These findings are independent from the study site, the number of training samples and specific classes. Consequently, the proposed IVM approach is a promising classification method for hyperspectral imagery.

@techreport{Roscher2012Evaluation,
  author = {Roscher, Ribana and Waske, Bj\"orn and F\"orstner, Wolfgang},
  title = {Evaluation of Import Vector Machines for Classifying Hyperspectral Data},
  year = {2012}
}

Ribana Roscher and Jan Siegemund and Falko Schindler and Wolfgang Förstner, "Object Tracking by Segmentation Using Incremental Import Vector Machines" 2012.

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We propose a framework for object tracking in image sequences, following the concept of tracking-by-segmentation. The separation of object and background is achieved by a consecutive semantic superpixel segmentation of the images, yielding tight object boundaries. I.e., in the first image a model of the object's characteristics is learned from an initial, incomplete annotation. This model is used to classify the superpixels of subsequent images to object and background employing graph-cut. We assume the object boundaries to be tight-fitting and the object motion within the image to be affine. To adapt the model to radiometric and geometric changes we utilize an incremental learner in a co-training scheme. We evaluate our tracking framework qualitatively and quantitatively on several image sequences.

@techreport{Roscher2012Object,
  author = {Roscher, Ribana and Siegemund, Jan and Schindler, Falko and F\"orstner, Wolfgang},
  title = {Object Tracking by Segmentation Using Incremental Import Vector Machines},
  year = {2012}
}

Ribana Roscher and Björn Waske and Wolfgang Förstner, "Incremental import vector machines for large area land cover classification", In IEEE International Conference on Computer Vision Workshops (ICCV Workshops). 2011.

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The classification of large areas consisting of multiple scenes is challenging regarding the handling of large and therefore mostly inhomogeneous data sets. Moreover, large data sets demand for computational efficient methods. We propose a method, which enables the efficient multi-class classification of large neighboring Landsat scenes. We use an incremental realization of the import vector machines, called I2VM, in combination with self-training to update an initial learned classifier with new training data acquired in the overlapping areas between neighboring Landsat scenes. We show in our experiments, that I2VM is a suitable classifier for large area land cover classification.

@inproceedings{Roscher2011Incremental,
  author = {Roscher, Ribana and Waske, Bj\"orn and F\"orstner, Wolfgang},
  title = {Incremental import vector machines for large area land cover classification},
  booktitle = {IEEE International Conference on Computer Vision Workshops (ICCV Workshops)},
  year = {2011},
  doi = {10.1109/ICCVW.2011.6130249}
}

Björn Waske and Ribana Roscher and Sascha Klemenjak, "Import Vector Machines Based Classification of Multisensor Remote Sensing Data", In IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2011.

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The classification of multisensor data sets, consisting of multitemporal SAR data and multispectral is addressed. In the present study, Import Vector Machines (IVM) are applied on two data sets, consisting of (i) Envisat ASAR/ERS-2 SAR data and a Landsat 5 TM scene, and (h) TerraSAR-X data and a RapidEye scene. The performance of IVM for classifying multisensor data is evaluated and the method is compared to Support Vector Machines (SVM) in terms of accuracy and complexity. In general, the experimental results demonstrate that the classification accuracy is improved by the multisensor data set. Moreover, IVM and SVM perform similar in terms of the classification accuracy. However, the number of import vectors is considerably less than the number of support vectors, and thus the computation time of the IVM classification is lower. IVM can directly be applied to the multi-class problems and provide probabilistic outputs. Overall IVM constitutes a feasible method and alternative to SVM.

@inproceedings{Waske2011Import,
  author = {Waske, Bj\"orn and Roscher, Ribana and Klemenjak, Sascha},
  title = {Import Vector Machines Based Classification of Multisensor Remote Sensing Data},
  booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
  year = {2011},
  doi = {10.1109/IGARSS.2011.6049829}
}

Mathias Hans and Ribana Roscher, "Zuordnen radiometrischer Informationen zu Laserscandaten von Weintrauben" 2011.

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In diesem Report stellen wir zwei Verfahren vor, die radiometrische Informationen 3D-Scandaten zuordnen. Radiometrische Informationen unterstützen und verbessern die Anwendungen der Merkmalserfassung von Objekten, da sie weitere Kenntnisse über das gescannte Objekt liefern.

@techreport{Hans2011Zuordnen,
  author = {Hans, Mathias and Roscher, Ribana},
  title = {Zuordnen radiometrischer Informationen zu Laserscandaten von Weintrauben},
  year = {2011}
}

Ribana Roscher and Falko Schindler and Wolfgang Förstner, "What would you look like in Springfield? Linear Transformations between High-Dimensional Spaces" 2011.

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High-dimensional data structures occur in many fields of computer vision and machine learning. Transformation between two high-dimensional spaces usually involves the determination of a large amount of parameters and requires much labeled data to be given. There is much interest in reducing dimensionality if a lower-dimensional structure is underlying the data points. We present a procedure to enable the determination of a low-dimensional, projective transformation between two data sets, making use of state-of-the-art dimensional reduction algorithms. We evaluate multiple algorithms during several experiments with different objectives. We demonstrate the use of this procedure for applications like classification and assignments between two given data sets. Our procedure is semi-supervised due to the fact that all labeled and unlabeled points are used for the dimensionality reduction, but only few them have to be labeled. Using test data we evaluate the quantitative and qualitative performance of different algorithms with respect to the classification and assignment task. We show that with these algorithms and our transformation approach high-dimensional data sets can be related to each other. Finally we can use this procedure to match real world facial images with cartoon images from Springfield, home town of the famous Simpsons.

@techreport{Roscher2011What,
  author = {Roscher, Ribana and Schindler, Falko and F\"orstner, Wolfgang},
  title = {What would you look like in Springfield? Linear Transformations between High-Dimensional Spaces},
  year = {2011}
}

Ribana Roscher and Falko Schindler and Wolfgang Förstner, "High Dimensional Correspondences from Low Dimensional Manifolds -- An Empirical Comparison of Graph-based Dimensionality Reduction Algorithms", In The 3rd International Workshop on Subspace Methods, in conjunction with ACCV2010., pp. 10. 2010.

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We discuss the utility of dimensionality reduction algorithms to put data points in high dimensional spaces into correspondence by learning a transformation between assigned data points on a lower dimensional structure. We assume that similar high dimensional feature spaces are characterized by a similar underlying low dimensional structure. To enable the determination of an affine transformation between two data sets we make use of well-known dimensional reduction algorithms. We demonstrate this procedure for applications like classification and assignments between two given data sets and evaluate six well-known algorithms during several experiments with different objectives. We show that with these algorithms and our transformation approach high dimensional data sets can be related to each other. We also show that linear methods turn out to be more suitable for assignment tasks, whereas graph-based methods appear to be superior for classification tasks.

@inproceedings{Roscher2010High,
  author = {Roscher, Ribana and Schindler, Falko and F\"orstner, Wolfgang},
  title = {High Dimensional Correspondences from Low Dimensional Manifolds -- An Empirical Comparison of Graph-based Dimensionality Reduction Algorithms},
  booktitle = {The 3rd International Workshop on Subspace Methods, in conjunction with ACCV2010},
  year = {2010},
  pages = {10},
  note = {Queenstown, New Zealand},
  doi = {10.1007/978-3-642-22819-3_34}
}

Ribana Roscher and Björn Waske and Wolfgang Förstner, "Kernel Discriminative Random Fields for land cover classification", In IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS). 2010.

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Logistic Regression has become a commonly used classifier, not only due to its probabilistic output and its direct usage in multi-class cases. We use a sparse Kernel Logistic Regression approach - the Import Vector Machines - for land cover classification. We improve our segmentation results applying a Discriminative Random Field framework on the probabilistic classification output. We consider the performance regarding to the classification accuracy and the complexity and compare it to the Gaussian Maximum Likelihood classification and the Support Vector Machines.

@inproceedings{Roscher2010Kernel,
  author = {Roscher, Ribana and Waske, Bj\"orn and F\"orstner, Wolfgang},
  title = {Kernel Discriminative Random Fields for land cover classification},
  booktitle = {IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)},
  year = {2010},
  note = {Istanbul, Turkey},
  doi = {10.1109/PRRS.2010.5742801}
}

Martin Drauschke and Ribana Roscher and Thomas Läbe and Wolfgang Förstner, "Improving Image Segmentation using Multiple View Analysis", In Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms and Evaluatin (CMRT09)., pp. 211-216. 2009.

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In our contribution, we improve image segmentation by integrating depth information from multi-view analysis. We assume the object surface in each region can be represented by a low order polynomial, and estimate the best fitting parameters of a plane using those points of the point cloud, which are mapped to the specific region. We can merge adjacent image regions, which cannot be distinguished geometrically. We demonstrate the approach for finding spatially planar regions on aerial images. Furthermore, we discuss the possibilities of extending of our approach towards segmenting terrestrial facade images.

@inproceedings{Drauschke2009Improving,
  author = {Drauschke, Martin and Roscher, Ribana and L\"abe, Thomas and F\"orstner, Wolfgang},
  title = {Improving Image Segmentation using Multiple View Analysis},
  booktitle = {Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms and Evaluatin (CMRT09)},
  year = {2009},
  pages = {211-216}
}

Ribana Roscher and Wolfgang Föerstner, "Multiclass Bounded Logistic Regression -- Efficient Regularization with Interior Point Method"(TR-IGG-P-2009-02) 2009.

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Logistic regression has been widely used in classication tasks for many years. Its optimization in case of linear separable data has received extensive study due to the problem of a monoton likelihood. This paper presents a new approach, called bounded logistic regression (BLR), by solving the logistic regression as a convex optimization problem with constraints. The paper tests the accuracy of BLR by evaluating nine well-known datasets and compares it to the closely related support vector machine approach (SVM).

@techreport{Roscher2009Multiclass,
  author = {Roscher, Ribana and F\"oerstner, Wolfgang},
  title = {Multiclass Bounded Logistic Regression -- Efficient Regularization with Interior Point Method},
  year = {2009},
  number = {TR-IGG-P-2009-02}
}

Ribana Roscher, "Lernen linearer probabilistischer diskriminativer Modelle für die semantische Bildsegmentierung". Thesis at: Institute of Photogrammetry, University of Bonn. 2008.

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[none]

@mastersthesis{Roscher2008Lernen,
  author = {Roscher, Ribana},
  title = {Lernen linearer probabilistischer diskriminativer Modelle f\"ur die semantische Bildsegmentierung},
  school = {Institute of Photogrammetry, University of Bonn},
  year = {2008},
  note = {Betreuung: Prof. Dr.-Ing. Wolfgang F\"orstner, Ing. Filip Kor{\vc}}
}

Ribana Roscher, "Bestimmung von 3D-Merkmalen von Bildregionen aus Stereobildern"(TR-IGG-P-2008-05) 2008.

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Dieser Report erläutert die Bestimmung von 3D-Merkmalen von Bildregionen durch Zuordnung von Bildpunkten in diesen Regionen zu den Objektpunkten in einer Punktwolke.Die Umsetzung erfolgt in einer grafischen Benutzeroberfläche in Matlab, deren Bedienung in diesem Report veranschaulicht werden soll.

@techreport{Roscher2008Bestimmung,
  author = {Roscher, Ribana},
  title = {Bestimmung von 3D-Merkmalen von Bildregionen aus Stereobildern},
  year = {2008},
  number = {TR-IGG-P-2008-05}
}