Mehdi Ellouze

Indexing Video Summaries for Quick Video Browsing

Multimedia data are used in many fields. The problem is how to manipulate the large quantity of these data. One of the proposed solutions is an intelligent video summarization system. Summarizing a video consists in providing another version that contains pertinent and important items. The most popular type of summary is the pictorial summary. We propose in this chapter to index pictorial summaries in order to accelerate the browsing operation of video archives. The chapter presents a conception of a digital video archive that offers three access levels making easier the search for video sequences. The first access level offers to the user a full access for the whole archive. The second access level allows to the user to browse video archive by consulting video summaries. We contribute by adding a third access level that accelerates the archive browsing by adding an indexing subsystem, which operates on video summaries. We propose to index video summaries to accelerate the research of desired sequences. We treat the case of news broadcast video

Toward an interactive device for quick news story browsing

In this paper, we present a new design for an interactive information service based on on-line recognition of the handwriting and quick news stories browsing. A person communicates with server PC using PDA and Bluetooth headset technology in order to consult same key frame that represent a summaries of video news. The result of the server research will by returned to the PDA.

Movie scenes detection with MIGSOM based on shots semi-supervised clustering

The segmentation into scenes helps users to browse movie archives and to select the interesting ones. In a given movie, we have two kinds of scenes: action scenes and non-action scenes. To detect action scenes, we rely on tempo features as motion and audio energy. However, to detect non-action scenes, we have to use the content information. In this paper, we present a new approach to detect non-action movie scenes. The main idea is the use of a new dynamic variant of the self-organizing maps called MIGSOM (Multilevel Interior Growing self-organizing maps) to detect agglomerations of shots in movie scenes. The originality of MIGSOM model lies in its architecture for evolving the structure of the network. The MIGSOM algorithm is generated by a growth process by adding nodes where it is necessary, whether from the boundaries or the interior of the map. In addition, the advantage of the proposed MIGSOM algorithm is their ability to find the best structure of the output space through the training process and to represent better the semantics of the data. Our system is tested on a varied database and compared to the classical SOM and others works. The obtained results show the merit of our approach in term of recall and precision rates and that our assumptions are well founded.

Regim, research group on intelligent machines, tunisia, at TRECVID 2008, BBC rushes summarization

In this paper, we describe our system used to summarize BBC rushes, the TRECVID database. Our summarization process starts with shot boundary detection. Then we filter obtained shots to retain only useful ones. After that we try to localize from every retained shot the important parts (sub-shots). Finally, we select some of them to formulate the skim. The selection of sub-shots must respond to many criteria as redundancy removing, covering all important events of the original video sequence and not exceeding the upper duration. Genetic algorithms are naturally suited for doing incremental selection. We use it to do the selection of relevant subs-shots. We consider the summarization process as an optimization problem which takes into consideration all evoked criteria. The obtained results are encouraging.

Social Networks Based Framework for Recommending Touristic Locations

Tourists need tools that can help them to select locations in which they can spend their holidays. We have multiple social networks in which we find information about hotels and about users’ experiences. The problem is how tourists can use this information to build their proper opinion about a particular location to decide if they should go to that place or not. We try in this paper to present a design of a solution that can be used to achieve this task. In this paper, we propose a framework for a recommender system that bases on opinions of persons on the one hand and on of users’ preferences on the other hand to generate recommendations. Indeed, opinions of tourists are extracted from different sources and analyzed to finally extract how the hotels are perceived by their customers in terms of features and activities. The final step consists in matching between these opinions and the users’ preferences to generate the recommendations. A prototype was developed in order to show how this framework is really working.