Osama El Demerdash

Text-Based Clustering of the ImageCLEFphoto Collection for Augmenting the Retrieved Results

We present our participation in the 2007 ImageCLEF photographic ad-hoc retrieval task. Our first participation in this years ImageCLEF comprised six runs. The main purpose of three of these runs was to evaluate the text and visual retrieval tools as well as their combination in the context of the given task. The other purpose of our participation was to experiment with applying clustering techniques to this task, which has not been done frequently in previous editions of the ImageCLEF Ad hoc task. We used the preclustered collection to augment the search results of the retrieval engines. For retrieval, we used two publicly available libraries; Apache Lucene for text and LIRE for visual retrieval. The clustered-augmented results reduced slightly the precision of the initial runs. While the aspired results have not yet been achieved, we note that the task is useful in assessing the validity of the clusters.

Image retrieval by inter-media fusion and pseudo-relevance feedback

This paper presents our participation at the ImageCLEF Photo 2008 task. We submitted six runs, experimenting with our own block-based visual retrieval as well as with query expansion. The results we obtained show that despite the poor performance of the visual and text retrieval components, better results can be obtained through pseudorelevance feedback and the inter-media fusion of the results.

Image query expansion using semantic selectional restriction

This paper describes our participation at ImageCLEF 2009. We participated in the photographic retrieval task (ImageCLEFPhoto). Our method is based on cross-media pseudo-relevance feedback. We have enhanced the pseudo-relevance feedback mechanism by using semantic selectional restrictions. We use Terrier for text retrieval and our own simple block-based visual retrieval engine. The results obtained at ImageCLEF 2009 show that our method is robust and promising. However, there is room for improvement on the visual retrieval.

Generating adaptive multimedia presentations based on a semiotic framework

We describe a prototype for generating adaptive multimedia presentations through the dynamic selection of files from a large data repository The presentation is generated based on a conceptual framework encompassing the technical (syntactic), semantic and relational textual annotation of the data as well as context-sensitive rules and patterns of selection discovered with the aid of the system during the preparation phase The prototype was developed using Java, Flash-MX and XML.

Semantic inter-media image retrieval in photographic collections

In this paper we propose a method for semantic inter-media photographic retrieval, exploiting the advantages of both textual and content-based frameworks: the relatively high initial precision and diversity of results from visual and text retrieval, and the robust overall precision and recall of text retrieval. The method employs simple block-based visual retrieval which, at early precision, outperforms MPEG-7 features ScalableColor, ColorLayout and EdgeHistogram, allowing for reliable auto-relevance feedback. The query expansion applied respects the semantic constraints of the original query and enhances precision and confidence through redundancy. The proposed method was tested on a benchmark data set of 20,000 diverse tourist photographs and obtained promising results.

Developing AMIE: an adaptive multimedia integrated environment

Large multimedia repositories can be used more effectively by providing a hybrid environment for accomplishing common tasks, such as searching, browsing, presenting and indexing of the material. In order to achieve this end, the semantic and relational information must accompany the technical, content-based data. In this paper, we present the framework underlying the development of a hybrid retrieval/presentation environment and a prototype in its early stages of development.