Some Results Using Different Approaches to Merge Visual and Text-Based Features in CLEF’08 Photo Collection

This paper describes the participation of the MIRACLE team at the ImageCLEF Photographic Retrieval task of CLEF 2008. We succeeded in submitting 41 runs. Obtained results from text-based retrieval are better than content-based as previous experiments in the MIRACLE team campaigns [5, 6] using different software. Our main aim was to experiment with several merging approaches to fuse text-based retrieval and content-based retrieval results, and it happened that we improve the text-based baseline when applying one of the three merging algorithms, although visual results are lower than textual ones

Late Semantic Fusion Approach for the Retrieval of Multimedia Data

In Multimedia information retrieval late semantic fusion is used to combine textual pre-filtering with an image re-ranking. Three steps are used for retrieval processes. Visual and textual techniques are combined to help the developed Multimedia Information Retrieval System to minimize the semantic gap for given query. In the paper, different late semantic fusion approaches i.e. Product, Enrich, MaxMerge and FilterN are used and for experiments publicly available ImageCLEF Wikipedia Collection is used. DOI: 10.17762/ijritcc2321-8169.150610

Multimedia Retrieval: Survey Of Methods And Approaches

As we know there are numbers of applications present where multimedia retrieval is used and also numbers of sources are present. So accuracy is the major issue in retrieval process. There are number of techniques and datasets available to retrieve information. Some techniques uses only text-based image retrieval (TBIR), some uses content-based image retrieval (CBIR) while some are using combination of both. In this paper we are focusing on both TBIR and CBIR results and then fusing these two results. For fusing we are using late fusion. TBIR captures conceptual meaning while CBIR used to avoid false results. So final results are more accurate. In this paper our main goal is to take review of different methods and approaches used for Multimedia Retrieval

Research in Linguistic Engineering: Resources and Tools

In this paper we are revisiting some of the resources and tools developed by the members of the Intelligent Systems Research Group (GSI) at UPM as well as from the Information Retrieval and Natural Language Processing Research Group (IR&NLP) at UNED. Details about developed resources (corpus, software) and current interests and projects are given for the two groups. It is also included a brief summary and links into open source resources and tools developed by other groups of the MAVIR consortium