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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 this paper we describe our TRECVID 2006 experiments. The MediaMill team participated in two tasks: concept detection and search. For concept detection we use the Medi-aMill Challenge as experimental platform. The MediaMill Challenge divides the generic video indexing problem into a visual-only, textual-only, early fusion, late fusion, and combined(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)
In this paper we describe our TRECVID 2005 experiments. The UvA-MediaMill team participated in four tasks. For the detection of camera work (runid: A CAM) we investigate the benefit of using a tessellation of detectors in combination with supervised learning over a standard approach using global image information. Experiments indicate that average precision(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)