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This paper provides an overview of the Social Event Detection (SED) task, which is organized as part of the MediaEval 2011 benchmarking activity. With the convergence between social networking and multimedia creation and distribution being experienced on a regular basis by hundreds of millions of people worldwide, this task examines how new or state of the(More)
In this paper, we propose a new type of multi-dimensional Hidden Markov Model based on the idea of Dependency Tree between positions. This simplification leads to an efficient implementation of the re-estimation algorithms, while keeping a mix of horizontal and vertical dependencies between positions. We explain DT-HMM and we present the formulas for the(More)
ÐThis paper presents a new compact shape representation for retrieving line-patterns from large databases. The basic idea is to exploit both geometric attributes and structural information to construct a shape histogram. We realize this goal by computing the N-nearest neighbor graph for the lines-segments for each pattern. The edges of the neighborhood(More)
This paper describes a graph-matching technique for recognising line-pattern shapes in large image databases. The methodological contribution of the paper is to develop a Bayesian matching algorithm that uses edge-consistency and node attribute similarity. This information is used to determine the a posteriori probability of a query graph for each of the(More)
We present a complete and efficient framework for video shot indexing and retrieval. Video shots are described by their key-frame, themselves described by their regions. Region-based approaches suffer from the complexity of segmentation and comparison tasks. A compact region-based shot representation is usually obtained thanks to vector-quantization method.(More)
This paper presents a novel multi-dimensional hidden Markov model approach to tackle the complex issue of image modeling. We propose a set of efficient algorithms that avoids the exponential complexity of regular multi-dimensional HMMs for the most frequent algorithms (Baum-Welch and Viterbi) due to the use of a random dependency tree (DT-HMM). We provide(More)