nils-herrmann/clean_scirepeval huggingface dataset:
Semi-supervised Image Segmentation by Parametric Distributional Clustering

The problem of semi-supervised image segmentation is frequently posed e.g. in remote sensing applications. In this setting, one aims at finding a decomposition of a given image into its constituent regions, which are typically assumed to have homogeneously distributed pixel values. In addition, it is requested that these regions can be equipped with some semantics, i.e. that they can be matched to particular land cover classes. For this purpose, class labels are provided for a small subset of the image data. The demand that the image segmentation respects those class labels implies that the segmentation algorithm should be posed as a constrained optimization problem.
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data

For several major applications of data analysis, objects are often not represented as feature vectors in a vector space, but rather by a matrix gathering pairwise proximities. Such pairwise data often violates metricity and, therefore, cannot be naturally embedded in a vector space. Concerning the problem of unsupervised structure detection or clustering, in this paper, a new embedding method for pairwise data into Euclidean vector spaces is introduced. We show that all clustering methods, which are invariant under additive shifts of the pairwise proximities, can be reformulated as grouping problems in Euclidian spaces. The most prominent property of this constant shift embedding framework is the complete preservation of the cluster structure in the embedding space. Restating pairwise clustering problems in vector spaces has several important consequences, such as the statistical description of the clusters by way of cluster prototypes, the generic extension of the grouping procedure to a discriminative prediction rule, and the applicability of standard preprocessing methods like denoising or dimensionality reduction.
Bagging for Path-Based Clustering

A resampling scheme for clustering with similarity to bootstrap aggregation (bagging) is presented. Bagging is used to improve the quality of path-based clustering, a data clustering method that can extract elongated structures from data in a noise robust way. The results of an agglomerative optimization method are influenced by small fluctuations of the input data. To increase the reliability of clustering solutions, a stochastic resampling method is developed to infer consensus clusters. A related reliability measure allows us to estimate the number of clusters, based on the stability of an optimized cluster solution under resampling. The quality of path-based clustering with resampling is evaluated on a large image data set of human segmentations.
Clustering with the Connectivity Kernel

Clustering aims at extracting hidden structure in dataset. While the problem of finding compact clusters has been widely studied in the literature, extracting arbitrarily formed elongated structures is considered a much harder problem. In this paper we present a novel clustering algorithm which tackles the problem by a two step procedure: first the data are transformed in such a way that elongated structures become compact ones. In a second step, these new objects are clustered by optimizing a compactness-based criterion. The advantages of the method over related approaches are threefold: (i) robustness properties of compactness-based criteria naturally transfer to the problem of extracting elongated structures, leading to a model which is highly robust against outlier objects; (ii) the transformed distances induce a Mercer kernel which allows us to formulate a polynomial approximation scheme to the generally NP-hard clustering problem; (iii) the new method does not contain free kernel parameters in contrast to methods like spectral clustering or mean-shift clustering.
A New Distance Measure for Probabilistic Shape Modeling

The contour of a planar shape is essentially one-dimensional signal embedded in 2-D space; thus the orthogonal distance, which only considers 1-D (norm) deviation from suggested models, is not rich enough to characterize the description quality of arbitrary model/shape pairs. This paper suggests a generalized distance measure, called Transport Distance, for probabilistic shape modeling. B-Spline primitives are used to represent models. The probability of a hypothetical model for a shape is determined on the basis of the new distance measure. Experiments show that an optimization procedure, which maximize the model probability, generates robust and visually pleasing geometric models for data.
A Compositionally Architecture for Perceptual Feature Grouping

We propose a compositionality architecture for perceptual organization which establishes a novel, generic, algorithmic framework for feature binding and condensation of semanticemm information in images. The underlying algorithmic ideas require a hierarchical structure for various types of objects and their groupings, which are guided by gestalt laws from psychology. A rich set of predefined feature detectors with uncertainty that perform real-valued measurements of relationships between objects can be combined in this flexible Bayesian framework. Compositions are inferred by minimizing the negative posterior grouping probability. The model structure is founded on the fundamental perceptual law of Pragnanz. The grouping algorithm performs hierarchical agglomerative clustering and it is rendered computationally feasible by visual pop-out. Evaluation on the edgel grouping task confirms the robustness of the architecture and its applicability to grouping in various visual scenarios.
On the Optimal Number of Clusters in Histogram Clustering

Clusters in data clustering should be robust to sample fluctuation, i.e., the estimate of cluster parameters on a second sample set should yield qualitatively similar results. This robustness requirement can be quantified by large deviation arguments from statistical learning theory. We use the principle of Empirical Risk Approximation to determine an optimal number of clusters for the case of histogram clustering. The analysis validates stochastic approximation algorithms like Markov Chain Monte Carlo which maximize the entropy for fixed optimization costs.
Stability-Based Model Order Selection in Clustering with Applications to Gene Expression Data

The concept of cluster stability is introduced to assess the validity of data partitionings found by clustering algorithms. It allows us to explicitly quantify the quality of a clustering solution, without being dependent on external information. The principle of maximizing the cluster stability can be interpreted as choosing the most self-consistent data partitioning. We present an empirical estimator for the theoretically derived stability index, based on resampling. Experiments are conducted on well known gene expression data sets, reanalyzing the work by Alon et al.  and by Spellman et al. .
Going Metric: Denoising Pairwise Data

Pairwise data in empirical sciences typically violate metricity, either due to noise or due to fallible estimates, and therefore are hard to analyze by conventional machine learning technology. In this paper we therefore study ways to work around this problem. First, we present an alternative embedding to multi-dimensional scaling (MDS) that allows us to apply a variety of classical machine learning and signal processing algorithms. The class of pair-wise grouping algorithms which share the shift-in variance property is statistically invariant under this embedding procedure, leading to identical assignments of objects to clusters. Based on this new vectorial representation, denoising methods are applied in a second step. Both steps provide a theoretically well controlled setup to translate from pairwise data to the respective denoised metric representation. We demonstrate the practical usefulness of our theoretical reasoning by discovering structure in protein sequence data bases, visibly improving performance upon existing automatic methods.
Stability-Based Model Selection

Model selection is linked to model assessment, which is the problem of comparing different models, or model parameters, for a specific learning task. For supervised learning, the standard practical technique is cross-validation, which is not applicable for semi-supervised and unsupervised settings. In this paper, a new model assessment scheme is introduced which is based on a notion of stability. The stability measure yields an upper bound to cross-validation in the supervised case, but extends to semi-supervised and unsupervised problems. In the experimental part, the performance of the stability measure is studied for model order selection in comparison to standard techniques in this area.
Parametric Distributional Clustering for Image Segmentation

Unsupervised Image Segmentation is one of the central issues in Computer Vision. From the viewpoint of exploratory data analysis, segmentation can be formulated as a clustering problem in which pixels or small image patches are grouped together based on local feature information. In this contribution, parametrical distributional clustering (PDC) is presented as a novel approach to image segmentation. In contrast to noise sensitive point measurements, local distributions of image features provide a statistically robust description of the local image properties. The segmentation technique is formulated as a generative model in the maximum likelihood framework. Moreover, there exists an insightful connection to the novel information theoretic concept of the Information Bottleneck (Tishby et al. ), which emphasizes the compromise between efficient coding of an image and preservation of characteristic information in the measured feature distributions.The search for good grouping solutions is posed as an optimization problem, which is solved by deterministic annealing techniques. In order to further increase the computational efficiency of the resulting segmentation algorithm, a multi-scale optimization scheme is developed. Finally, the performance of the novel model is demonstrated by segmentation of color images from the Corel data base.
Self-organized Clustering of Mixture Models for Combined Color And Texture Segmentation

The segmentation of images based on color and texture cues is formulated as a clustering problem in the joint color and texture space. Small image patches are grouped together on the basis of local color and spatial frequency statistics which is captured by Gaussian mixture models in feature space. The locality of segments in feature space is taken into account by the topological organization of the cluster structure which corresponds to self-organizing maps. The clusters define a one dimensional chain in the space of mixture models, which favors an ordering of the groups with similar color and texture distributions. The probabilistic model is tested by segmentation experiments on images from the Corel data base.
Data Resampling for Path Based Clustering

Path Based Clustering assigns two objects to the same cluster if they are connected by a path with high similarity between adjacent objects on the path. In this paper, we propose a fast agglomerative algorithm to minimize the Path Based Clustering cost function. To enhance the reliability of the clustering results a stochastic resampling method is used to generate candidate solutions which are merged to yield empirical assignment probabilities of objects to clusters. The resampling algorithm measures the reliability of the clustering solution and, based on their stability, determines the number of clusters.
Supplementary Information: Ellipsoidal Multiple Instance Learning

Proof of Lemma 3. We make use of the following mul-tivariate Chebyshev's inequality to proof Lemma 3 sup y∼(¯ y,Σy) Pr[y ∈ S] = 1 1 + d 2 , with d 2 = inf y∈S (y − ¯ y) Σ −1 y (y − ¯ y) (1) Where y is a random vector, the supremum is over all distributions for y with mean ¯ y and covariance matrix Σ y and S is a given convex set.
Combined color and texture segmentation by parametric distributional clustering

Unsupervised image segmentation can be formulated as a clustering problem in which pixels or small image patches are grouped together based on local feature information. In this contribution, parametric distributional clustering (PDC) is presented as a novel approach to image segmentation based on color and texture clues. The objective function of the PDC model is derived from the recently proposed Information Bottleneck framework (Tishby et al., 1999), but it can equivalently be formulated in terms of a maximum likelihood solution. Its optimization is performed by deterministic annealing. Segmentation results are shown for natural wildlife imagery.
A Resampling Approach to Cluster Validation

The concept of cluster stability is introduced as a means for assessing the validity of data partitionings found by clustering algorithms. It allows us to explicitly quantify the quality of a clustering solution, without being dependent on external information. The principle of maximizing the cluster stability can be interpreted as choosing the most self-consistent data partitioning. We present an empirical estimator for the theoretically derived stability index, based on imitating independent sample-sets by way of resampling. Experiments on both toy-examples and real-world problems effectively demonstrate that the proposed validation principle is highly suited for model selection.
Contextual classification by entropy-based polygonization

To improve the performance of pixel-wise classification results for remotely sensed imagery, several contextual classification schemes have been proposed that aim at avoiding classification noise by local averaging. These algorithms, however, bear the serious disadvantage of smoothing the segment boundaries and producing rounded segments that hardly match the true shapes. The authors present a novel contextual classification algorithm that overcomes these shortcomings. Using a hierarchical approach for generating a triangular mesh, it decomposes the image into a set of polygons that, in our application, represent individual land-cover types. Compared to classical contextual classification approaches, this method has the advantage of generating output that matches the intuitively expected type of segmentation. Besides, it achieves excellent classification results.
Topology free hidden Markov models: application to background modeling

Hidden Markov models (HMMs) are increasingly being used in computer vision for applications such as: gesture analysis, action recognition from video, and illumination modeling. Their use involves an off-line learning step that is used as a basis for on-line decision making (i.e. a stationarity assumption on the model parameters). But, real-world applications are often non-stationary in nature. This leads to the need for a dynamic mechanism to learn and update the model topology as well as its parameters. This paper presents a new framework for HMM topology and parameter estimation in an online, dynamic fashion. The topology and parameter estimation is posed as a model selection problem with an MDL prior. Online modifications to the topology are made possible by incorporating a state splitting criterion. To demonstrate the potential of the algorithm, the background modeling problem is considered. Theoretical validation and real experiments are presented.
Multiscale Annealing and Robustness: Fast Heuristics for Large Scale Non-linear Optimization

Multiscale Annealing is an extension of the idea of deterministic annealing which link the approximation quality of the algorithms to their effective spatial resolution. The optimization variables of a particular scale are linked together to reduce the spatial resolution, and, thereby, to simplify the computational complexity of the optimization task. Robustness and efficiency issues are discussed in this article on research questions of annealing techniques.
The Noisy Euclidean Traveling Salesman Problem and Learning

We consider noisy Euclidean traveling salesman problems in the plane, which are random combinatorial problems with underlying structure. Gibbs sampling is used to compute average trajectories, which estimate the underlying structure common to all instances. This procedure requires identifying the exact relationship between permutations and tours. In a learning setting, the average trajectory is used as a model to construct solutions to new instances sampled from the same source. Experimental results show that the average trajectory can in fact estimate the underlying structure and that overfitting effects occur if the trajectory adapts too closely to a single instance.
Coupled Clustering: A Method for Detecting Structural Correspondence

This paper proposes a new paradigm and a computational framework for revealing equivalencies (analogies) between sub-structures of distinct composite systems that are initially represented by unstructured data sets. For this purpose, we introduce and investigate a variant of traditional data clustering, termed coupled clustering, which outputs a configuration of corresponding subsets of two such representative sets. We apply our method to synthetic as well as textual data. Its achievements in detecting topical correspondences between textual corpora are evaluated through comparison to performance of human experts.
Path Based Pairwise Data Clustering with Application to Texture Segmentation

Most cost function based clustering or partitioning methods measure the compactness of groups of data. In contrast to this picture of a point source in feature space, some data sources are spread out on a low-dimensional manifold which is embedded in a high dimensional data space. This property is adequately captured by the criterion of connectedness which is approximated by graph theoretic partitioning methods. 
 
We propose in this paper a pairwise clustering cost function with a novel dissimilarity measure emphasizing connectedness in feature space rather than compactness. The connectedness criterion considers two objects as similar if there exists a mediating intra cluster path without an edge with large cost. The cost function is optimized in a multi-scale fashion. This new path based clustering concept is applied to segment textured images with strong texture gradients based on dissimilarities between image patches.
Basic Principles of Annealing for Large Scale Non-Linear Optimization

Computational Annealing, a class of optimization heuristics that are inspired by statistical physics of phase transitions has been demonstrated to be highly effective for large, non-linear combinatorial optimization problems. In many applications in computer vision and pattern recognition one encounters non-linear objective functions with a very large number of discrete and possibly additional continuous variables. Typical cases of such problems are clustering, grouping and image segmentation or assignment problems in motion or stereo analysis or in object recognition. For this type of problems, standard integer programming techniques are not applicable and one has to resort to optimization heuristics that are fast, yet avoid a possibly exponential number of unfavorable local minima. A particularly powerful, generic class of algorithms is provided by simulated or deterministic annealing techniques. Simulated annealing and the Gibbs sampler are discussed first to present the basic concepts; then, the theory of deterministic annealing is presented in great detail and the relation to continuation methods are discussed.
A New Adaptive Algorithm for the Polygonization of Noisy Imagery

This report introduces a novel adaptive image segmentation algorithm which represents images by polygonal segments and which is particularly suitable for the segmentation of noisy imagery. At first we suggest an intuitive generative model and its associated cost function. The cost function can effectively be optimized by a hierarchical triangulation algorithm, which iteratively refines and reorganizes a triangular mesh and finally provides a compact description of the essential image structure. After analyzing fundamental properties of our cost function, we adapt an information-theoretic bound to assess the statistical significance of a given triangulation step. The bound effectively defines a stopping criterion to limit the number of triangles in the mesh, thereby avoiding undesirable overfitting phenomena. Besides, it facilitates the development of a multi-scale variant of the triangulation algorithm, which substantially decreases its computational demands. The algorithm has various important applications in contextual classification, remote sensing, and visual object recognition.
Topology Free Hidden Markov Models: Application to Background Modeling

Hidden Markov Models (HMMs) are increasingly being used in computer vision for applications such as: gesture analysis, action recognition from video, and illumination modeling. Their use involves an off-line learning step that is used as a basis for on-line decision making (i.e. a stationarity assumption on the model parameters). But, realworld applications are often non-stationary in nature. This leads to the need for a dynamic mechanism to learn and update the model topology as well as its parameters. This paper presents a new framework for HMM topology and parameter estimation in an online, dynamic fashion. The topology and parameter estimation is posed as a model selection problem with an MDL prior. Online modifications to the topology are made possible by incorporating a state splitting criterion. To demonstrate the potential of the algorithm, the background modeling problem is considered. Theoretical validation and real experiments are presented.
Empirical Evaluation of Dissimilarity Measures for Color and Texture

This paper empirically compares nine families of image dissimilarity measures that are based on distributions of color and texture features summarizing over 1000 CPU hours of computational experiments. Ground truth is collected via a novel random sampling scheme for color, and by an image partitioning method for texture. Quantitative performance evaluations are given for classification, image retrieval, and segmentation tasks, and for a wide variety of dissimilarity measure parameters. It is demonstrated how the selection of a measure, based on large scale evaluation, substantially improves the quality of classification, retrieval, and unsupervised segmentation of color and texture images.
A New Contour-Based Approach to Object Recognition for Assembly Line Robots

A complete processing chain for visual object recognition is described in this paper. The system automatically detects individual objects on an assembly line, identifies their type, position, and orientation, and, thereby, forms the basis for automated object recognition and manipulation by single-arm robots. Two new ideas entered into the design of the recognition system. First we introduce a new fast and robust image segmentation algorithm that identifies objects in an unsupervised manner and describes them by a set of closed polygonal lines. Second we describe how to embed this object description into an object recognition process that classifies the objects by matching them to a given set of prototypes. Furthermore, the matching function allows us to determine the relative orientation and position of an object. Experimental results for a representative set of real-world tools demonstrate the quality and the practical applicability of our approach.