Dimitrios Besiris

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In this work the normalized dictionary distance (NDD) is presented and investigated. NDD is a similarity metric based on the dictionary of a sequence acquired from a data compressor. A dictionary gives significant information about the structure of the sequence it has been extracted from. We examine the performance of this new distance measure for color(More)
In this work, the idea of key frames extraction from single shots in video sequences is presented. The method is implemented by an efficient two-step algorithm, which is classified neither to clustering nor to temporal variations based techniques. In the first step, an MST (minimal spanning tree) graph is constructed, where each node is associated to a(More)
The paper presents an automatic video summarization technique based on graph theory methodology and the dominant sets clustering algorithm. The large size of the video data set is handled by exploiting the connectivity information of prototype frames that are extracted from a down-sampled version of the original video sequence. The connectivity information(More)
In this work, we propose a unified approach for video summarization based on the analysis of the video structure. The method originates from a data learning technique that uses the membership values produced by an over-partitioning mode of the FCM algorithm to find the connection strength between the resulting set of prototype centers. The final clustering(More)
In this work, the idea of local features extraction from image data based on points of interest, is revised. The method is based on a nonparametric pairwise clustering algorithm and the application of Hubert's test statistic. The clustering algorithm iteratively partitions the input image data until it finally converges to 2 classes. On the other hand the(More)
A fast, simple and low complexity technique for image database organization is presented. The basic idea is to reveal the connectivity relations of the database obtain information of the database structure and facilitate the clustering process. This is achieved by randomly selecting a certain number of prototype data and using appropriately the membership(More)
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