Cor J. Veenman

Resolving motion correspondence for densely moving points

Studies the motion correspondence problem for which a diversity of qualitative and statistical solutions exist. We concentrate on qualitative modeling, especially in situations where assignment conflicts arise either because multiple features compete for one detected point or because multiple detected points fit a single feature point. We leave out the possibility of point track initiation and termination because that principally conflicts with allowing for temporary point occlusion. We introduce individual, combined, and global motion models and fit existing qualitative solutions in this framework. Additionally, we present a tracking algorithm that satisfies these-possibly constrained-models in a greedy matching sense, including an effective way to handle detection errors and occlusion. The performance evaluation shows that the proposed algorithm outperforms existing greedy matching algorithms. Finally, we describe an extension to the tracker that enables automatic initialization of the point tracks. Several experiments show that the extended algorithm is efficient, hardly sensitive to its few parameters, and qualitatively better than other algorithms, including the presumed optimal statistical multiple hypothesis tracker.

A maximum variance cluster algorithm

We present a partitional cluster algorithm that minimizes the sum-of-squared-error criterion while imposing a hard constraint on the cluster variance. Conceptually, hypothesized clusters act in parallel and cooperate with their neighboring clusters in order to minimize the criterion and to satisfy the variance constraint. In order to enable the demarcation of the cluster neighborhood without crucial parameters, we introduce the notion of foreign cluster samples. Finally, we demonstrate a new method for cluster tendency assessment based on varying the variance constraint parameter.

A protocol for building and evaluating predictors of disease state based on microarray data

MOTIVATIONnMicroarray gene expression data are increasingly employed to identify sets of marker genes that accurately predict disease development and outcome in cancer. Many computational approaches have been proposed to construct such predictors. However, there is, as yet, no objective way to evaluate whether a new approach truly improves on the current state of the art. In addition no standard computational approach has emerged which enables robust outcome prediction.nnnRESULTSnAn important contribution of this work is the description of a principled training and validation protocol, which allows objective evaluation of the complete methodology for constructing a predictor. We review the possible choices of computational approaches, with specific emphasis on predictor choice and reporter selection strategies. Employing this training-validation protocol, we evaluated different reporter selection strategies and predictors on six gene expression datasets of varying degrees of difficulty. We demonstrate that simple reporter selection strategies (forward filtering and shrunken centroids) work surprisingly well and outperform partial least squares in four of the six datasets. Similarly, simple predictors, such as the nearest mean classifier, outperform more complex classifiers. Our training-validation protocol provides a robust methodology to evaluate the performance of new computational approaches and to objectively compare outcome predictions on different datasets.

The nearest subclass classifier: a compromise between the nearest mean and nearest neighbor classifier

We present the nearest subclass classifier (NSC), which is a classification algorithm that unifies the flexibility of the nearest neighbor classifier with the robustness of the nearest mean classifier. The algorithm is based on the maximum variance cluster algorithm and, as such, it belongs to the class of prototype-based classifiers. The variance constraint parameter of the cluster algorithm serves to regularize the classifier, that is, to prevent overfitting. With a low variance constraint value, the classifier turns into the nearest neighbor classifier and, with a high variance parameter, it becomes the nearest mean classifier with the respective properties. In other words, the number of prototypes ranges from the whole training set to only one per class. In the experiments, we compared the NSC with regard to its performance and data set compression ratio to several other prototype-based methods. On several data sets, the NSC performed similarly to the k-nearest neighbor classifier, which is a well-established classifier in many domains. Also concerning storage requirements and classification speed, the NSC has favorable properties, so it gives a good compromise between classification performance and efficiency.

A cellular coevolutionary algorithm for image segmentation

Clustering is inherently a difficult problem, both with respect to the definition of adequate models as well as to the optimization of the models. We present a model for the cluster problem that does not need knowledge about the number of clusters a priori. This property is among others useful in the image segmentation domain, which we especially address. Further, we propose a cellular coevolutionary algorithm for the optimization of the model. Within this scheme multiple agents are placed in a regular two-dimensional (2-D) grid representing the image, which imposes neighboring relations on them. The agents cooperatively consider pixel migration from one agent to the other in order to improve the homogeneity of the ensemble of the image regions they represent. If the union of the regions of neighboring agents is homogeneous then the agents form alliances. On the other hand, if an agent discovers a deviant subject, it isolates the subject. In the experiments we show the effectiveness of the proposed method and compare it to other segmentation algorithms. The efficiency can easily be improved by exploiting the intrinsic parallelism of the proposed method.

LESS: a model-based classifier for sparse subspaces

In this paper, we specifically focus on high-dimensional data sets for which the number of dimensions is an order of magnitude higher than the number of objects. From a classifier design standpoint, such small sample size problems have some interesting challenges. The first challenge is to find, from all hyperplanes that separate the classes, a separating hyperplane which generalizes well for future data. A second important task is to determine which features are required to distinguish the classes. To attack these problems, we propose the LESS (lowest error in a sparse subspace) classifier that efficiently finds linear discriminants in a sparse subspace. In contrast with most classifiers for high-dimensional data sets, the LESS classifier incorporates a (simple) data model. Further, by means of a regularization parameter, the classifier establishes a suitable trade-off between subspace sparseness and classification accuracy. In the experiments, we show how LESS performs on several high-dimensional data sets and compare its performance to related state-of-the-art classifiers like, among others, linear ridge regression with the LASSO and the support vector machine. It turns out that LESS performs competitively while using fewer dimensions.

A fast and robust point tracking algorithm

We present an algorithm that efficiently tracks a predefined set of landmark points in a time sequence of images. The algorithm iteratively optimizes the correspondences between the point measurements in the images, while allowing for spurious and missing point measurements. This trajectory based approach hypothesizes missing points by interpolation. Spurious measurements are either left our because they do not form the optimal correspondences or are removed afterwards if they have the smoothness or other constraint exceed its predetermined maximum.

Motion tracking as a constrained optimization problem

In this paper we pose the problem of tracking of a varying number of points through an image sequence as a multi-objective optimization problem with additional hard constraints. One of the objectives is to find smooth tracks based on second-order motion characteristics optimized over several frames. The corresponding optimization algorithm we present is a sequential heuristic search algorithm that adequately prunes the search tree in such a way that its exponential order remains low. When the algorithm is compared to other tracking algorithms, it turns out that the proposed algorithm is easier to tune and generally more efficient and more accurate.

Establishing motion correspondence using extended temporal scope

This paper addresses the motion correspondence problem: the problem of finding corresponding point measurements in an image sequence solely based on positional information. The motion correspondence problem is most difficult when the target points are densely moving. It becomes even harder when the point detection scheme is imperfect or when points are temporarily occluded. Available motion constraints should be exploited in order to rule out physically impossible assignments of measurements to point tracks. The performance can be further increased by deferring the correspondence decisions, that is, by examining whether the consequences of candidate correspondences lead to alternate and better solutions. In this paper, we concentrate on the latter by introducing a scheme that extends the temporal scope over which the correspondences are optimized. The consequent problem we are faced with is a multi-dimensional assignment problem, which is known to be NP-hard. To restrict the consequent increase in computation time, the candidate solutions are suitably ordered and then additional combined motion constraints are imposed. Experiments show the appropriateness of the proposed extension, both with respect to performance as well as computational aspects.

Groups of Collaborating Users and Agents in Ambient Intelligent Environments

An ad hoc agent environment is a way for users to interact with an ambient intelligent environment. Agents are associated with every device, service or content. The user interacts with his environment as a whole, instead of interacting with individual applications on individual devices. Devices and services in the environment have to be more or less independent, which fits well with the notion that agents are autonomous. A research application that demonstrates some of the user agent interaction issues involved when employing agents has been developed. It shows the tension between the user being in control and the autonomy of agents. The notion of cooperating groups is introduced as a way for users to gain control over which agents collaborate. Users can then establish connections between devices and content that are meaningful to them, in the context of their task.