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This paper introduces a method for scene categorization by modeling ambiguity in the popular codebook approach. The codebook approach describes an image as a bag of discrete visual codewords, where the frequency distributions of these words are used for image categoriza-tion. There are two drawbacks to the traditional codebook model: code-word uncertainty(More)
This paper studies automatic image classification by modeling soft assignment in the popular codebook model. The codebook model describes an image as a bag of discrete visual words selected from a vocabulary, where the frequency distributions of visual words in an image allow classification. One inherent component of the codebook model is the assignment of(More)
This paper studies the motion correspondence problem for which a diversity of qualitative and statistical solutions exist. We concentrate on qualitative modeling, especially for 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(More)
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(More)
In the face of current large-scale video libraries, the practical applicability of content-based indexing algorithms is constrained by their efficiency. This paper strives for efficient large-scale video indexing by comparing various visual-based concept categorization techniques. In visual categorization, the popular codebook model has shown excellent(More)
—Whereas video tells a narrative by a composition of shots, current video retrieval methods focus mainly on single shots. In retrieval performance estimation, similar shots in a narrative may result in performance over-estimation. We propose an episode-based version of cross-validation leading up to 14% classification improvement over shot based(More)
We present a generic and robust approach for scene cate-gorization. A complex scene is described by proto-concepts like vegetation, water, fire, sky etc. These proto-concepts are represented by low level features, where we use natural images statistics to compactly represent color invariant texture information by a Weibull distribution. We introduce the(More)
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(More)
MOTIVATION Microarray 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(More)