Catálogo de publicaciones - libros

With the proliferation of camera phones, many novel applications and services are emerging. In this paper, we present the SnapToTell system, which provides information directory service to tourists, based on pictures taken by the camera phones and location information. We present also experimental results on scene recognition based on a realistic data set of scenes and locations in Singapore which form a new original application oriented image test bed freely available.

Bilingual dictionaries are essential components of cross-lingual information retrieval applications. The automatic acquisition of proper names and their translations from bilingual corpora is especially important, because a significant portion of the entries not listed in the dictionaries would be proper names.

In this paper we propose to combine IR and OLAP (On-Line Analytical Processing) technologies to exploit a warehouse of text-rich XML documents. In the system we plan to develop, a multidimensional implementation of a relevance modeling document model will be used for interactively querying the warehouse by allowing navigation in the structure of documents and in a concept hierarchy of query terms. The facts described in the relevant documents will be ranked and analyzed in a novel OLAP cube model able to represent and manage facts with relevance indexes.

Collaborative search refers to how the search behavior of communities of users can be used to influence the ranking of search results. In this poster we describe how this technique, as instantiated in the I-SPY meta-search engine can be used as a general mechanism for implementing a different relevance feedback strategy. We evaluate a relevance feedback strategy based on anchor-text and query similarity using the TREC2004 Terabyte track document collection.

As a method for information retrieval (IR) on the Web, search engines have become the tool of choice for most online users. However, despite the variety of next generation approaches to Web search we have seen recently (e.g. [1,2]), the problems of information overload, vague user queries and spam still have the effect that many search sessions end in user frustration. Generally search engines are criticised for returning result lists that have low precision, where the user’s information need is not satisfied by any of the returned result pages.

Mobile devices suffer from limited screen real-estate and restricted text input capabilities. In the recent past these limitations have greatly effected the usability of many mobile Internet applications [1], largely because little effort has been typically made to take account of the special features of the mobile Internet. These limitations are especially problematic for mobile search-engines: they restrict the number of results that can be displayed per screen and impact the type of queries that are likely to be provided. Nevertheless, most attempts to provide mobile search engines have involved making only simplistic adaptations to standard search interfaces. For example, fewer results per page are returned and the ‘snippet’ text associated with each result may be truncated [2]. We believe that more fundamental adaptations are necessary if search technology is to succeed in the mobile space. In this paper we focus on the snippet text issue and we argue that providing paragraphs of descriptive text alongside each result is a luxury that does not make sense in the context of mobile device limitations. We describe how the I-SPY system [3] can track and record past queries that have resulted in the selection of a given result page and we argue that these related queries can be used to help users understand the context of a search result in place of more verbose snippet text.

Associating concepts to video segments is essential for content-based video retrieval. We present here a semantic classifier working from text transcriptions coming from automatic speech recognition (ASR). The system is based on a Bayesian classifier, it is fully linked with a knowledge base which contains an ontology and named entities from several domains. The system is trained from a set of positive and negative examples for each indexed concept. It has been evaluated using the TREC VIDEO protocol and conditions for the detection of visual concepts. Three versions are compared: a baseline one, using only word as units, a second, using additionally named entities, and a last one enriched with semantic classes information.

Much research has been performed investigating how links between web pages can be exploited in an Information Retrieval setting [1,4]. In this poster, we investigate the application of the Barabási-Albert model to link structure analysis on a collection of web documents within the language modeling framework. Our model utilizes the web structure as described by a Scale Free Network and derives a document prior based on a web document’s age and linkage. Preliminary experiments indicate the utility of our approach over other current link structure algorithms and warrants further research.

In visual information retrieval, a exists due to the poor match between machine-understood content of an information object and the userpercepted one. The mismatch of perception results in di.culties for a user in formulating the query, and consequently in inability for the retrieval system to produce satisfactory answers. Adding searcher’s relevance judgements for (intermediary) search results is known to improve the retrieval. With relevance feedback the system learns the user’s information need through interaction.

The phenomenon that conceptually related shots appear together in videos is called . This phenomenon is a useful cue for video concept detection, which is one of basic steps in content-based video indexing and retrieval. We propose a method, called , to improve results of video concept detection by exploiting the temporal shot clustering phenomenon. Two other methods are compared with temporal shot clustering analysis on the TRECVID 2003 dataset. Experiments showed that temporal shot clustering is of much benefit for video concept detection, and that temporal shot clustering method outperforms the other methods.