Handling Temporal Heterogeneous Data for Content-Based Management of Large Video Collections

Video document retrieval is now an active part of the domain of multimedia retrieval. However, unlike for other media, the management of a collection of video documents adds the problem of efficiently handling an overwhelming volume of temporal data. Challenges include balancing efficient content modeling and storage against fast access at various levels. In this paper, we detail the framework we have built to accommodate our developments in content-based multimedia retrieval. We show that not only our framework facilitates the development of processing and indexing algorithms but it also opens the way to several other possibilities such as rapid interface prototyping or retrieval algorithm benchmarking. Here, we discuss our developments in relation to wider contexts such as MPEG-7 and the TREC Video Track

Orion: A Generic Model and Tool for Data Mining

International audienc

Stochastic Algorithms For Exploratory Data Analysis: Data Clustering And Data Visualization

. Iterative, EM-type algorithms for data clustering and data visualization are derived on the basis of the maximum entropy principle. These algorithms allow the data analyst to detect structure in vectorial or relational data. Conceptually, the clustering and visualization procedures are formulated as combinatorial or continuous optimization problems which are solved by stochastic optimization. 1. INTRODUCTION Exploratory Data Analysis addresses the question of how to discover and model structure hidden in a data set. Data clustering (JD88) and data visualization are important algorithmic tools in this quest for explanation of data relations. The structural relationships between data points, e.g., pronounced similarity of groups of data vectors, have to be detected in an unsupervised fashion. This search for prototypes poses a delicate tradeoff: a sufficiently rich modelling approach should be able to capture the essential structure in a data set but we should restrain ourselves from ..