Abstract

Abstract

Abstract

Abstract
Multimedia databases are extending the scope of traditional databases to handle the complex structure of multimedia objects. Models for multimedia information must include representations for the structure and content of several media in a form that allows flexibility in retrieval. Content-based retrieval is the main motivation behind recent research in multimedia databases. The task of searching in video and audio content is made hard by the nature of audiovisual data where, unlike text, there is no direct syntactic channel between the object and its meaning [1]-[3]. We propose a model for multimedia content storage and retrieval accounting for both context and content information and taking advantage of their dependencies for effective retrieval. We then describe a prototype multimedia database with a retrieval interface. It has been used as a workbench for testing the representation model and integrating tools for feature extraction, information interchange and retrieval. The workbench allows an easy inclusion of new tools for content analysis and new methods for context- and content-based retrieval while offering storage and access for both the actual digital content and its metadata.

Abstract
Wikis are popular tools commonly used to support distributed collaborative work. Wikis can be seen as virtual scrap-books that anyone can edit without having any specific technical know-how. The Wikipedia is a flagship example of a real-word application of wikis. Due to the large scale of Wikipedia it's difficult to easily grasp much of the information that is stored in this wiki. We address one particular aspect of this issue by looking at the revision history of each article. Plotting the revision activity in a timeline we expose the complete article's history in a easily understandable format. We present WIKICHANGES, a web-based application designed to plot an article's revision timeline in real time. WIKICHANGES also includes a web browser extension that incorporates activity sparklines in the real Wikipedia. Finally, we introduce a revisions summarization task that addresses the need to understand what occurred during a given set of revisions. We present a first approach to this task using tag clouds to present the revisions made. © 2008 ACM.

Abstract
The INESC Porto group has participated in the search task (automatic and interactive). Our approach combines high-level features (the 39 con- cepts of the LSCOM-Lite set) with low-level features. We use a large set of low-level features with the intention of analysing as many facets as possible of each shot. The aggregation of large feature sets can be time consuming as it needs to be done at query time. We have developed the BitMatrix indexing method to speed up the search process. For each shot, binary signatures in the form of bit sequences are obtained in an on-line process. At query time, the query bit signature is compared to each of the shots signatures. The automatic run performs above the median, in spite of not using any classifier or any other knowledge sources except the translation of the query into LSCOM-Lite concepts.