Impact of image organizations on multimedia document retrieval

In this paper we compare ranking effectiveness of heterogeneous multimedia document retrieval when different image organizations are used for formulating queries. The quality of image queries depends on the organization of images used to make queries which in turn significantly impacts retrieval precision. CBIR (content based information retrieval) needs an effective and efficient organization of images including user interface which must be part of the configuration parameters of image retrieval research. <br /

Image featuring for retrieval of multimedia documents

The utilization of massive multimedia documents collections, such as multimedia documents in the global Internet, needs search engines which can rank using both text and image evidence. Massive size and (dynamic) nature of collection can make manual indexing prohibitively expensive in such situations. Traditional search engines utilize only text components of multimedia documents. But there are information needs, which require the utilization of image evidence. In this paper, we investigate image-feature for large and heterogeneous collections. Both the nature and complexities of information needs are key elements for an effective retrieval. Retrieval needs that depend on perceptual similarities (as found in art galleries, building architecture) require the utilization of visual cues. In such situations, the retrieval of multimedia document based on image ranking can provide higher effectiveness. Experimental results show that effectiveness of ranking based on image feature can be higher where perceptual similarities are key elements for retrieval than the retrieval effectiveness of algorithms based on text ranking algorithms <br /

PAC-Bayesian Majority Vote for Late Classifier Fusion

A lot of attention has been devoted to multimedia indexing over the past few years. In the literature, we often consider two kinds of fusion schemes: The early fusion and the late fusion. In this paper we focus on late classifier fusion, where one combines the scores of each modality at the decision level. To tackle this problem, we investigate a recent and elegant well-founded quadratic program named MinCq coming from the Machine Learning PAC-Bayes theory. MinCq looks for the weighted combination, over a set of real-valued functions seen as voters, leading to the lowest misclassification rate, while making use of the voters' diversity. We provide evidence that this method is naturally adapted to late fusion procedure. We propose an extension of MinCq by adding an order- preserving pairwise loss for ranking, helping to improve Mean Averaged Precision measure. We confirm the good behavior of the MinCq-based fusion approaches with experiments on a real image benchmark.Comment: 7 pages, Research repor

Design and Implementation of World Wide Web Pages for the Computational Information Systems Research Group in the Department of Information Studies, University of Sheffield

This dissertation consists of six chapters, and describes the design and implementation of the World Wide Web pages for the Computational Information Systems Research Group in the Department of Information Studies. The idea of implementing the World Wide Web pages for the research group is to get the information of the group published on the Internet. The area of networking (Internet) is reviewed particularly with reference to the World Wide Web. A few network tools are discussed. The emphasis is given more on the World Wide Web and the browser used as these form the most important part in this project. The Web pages were created via the HTML editor called the HTML Writer. The HTML language used in writing the program is described together with a brief description of the functions used in the program. A few number of hyper-editing systems are also discussed, with most attention being given to the hyper-editing system used in this project. The comparison was made between HTML Writer and the Hot-Metal Editor. The design and development of the Web pages is described from the first attempts to write the program to the final version. Lastly, the Web pages are reviewed as the final step in completing this project

ASLP-MULAN: Audio speech and language processing for multimedia analytics

Our intention is generating the right mixture of audio, speech and language technologies with big data ones. Some audio, speech and language automatic technologies are available or gaining enough degree of maturity as to be able to help to this objective: automatic speech transcription, query by spoken example, spoken information retrieval, natural language processing, unstructured multimedia contents transcription and description, multimedia files summarization, spoken emotion detection and sentiment analysis, speech and text understanding, etc. They seem to be worthwhile to be joined and put at work on automatically captured data streams coming from several sources of information like YouTube, Facebook, Twitter, online newspapers, web search engines, etc. to automatically generate reports that include both scientific based scores and subjective but relevant summarized statements on the tendency analysis and the perceived satisfaction of a product, a company or another entity by the general population