Catálogo de publicaciones - libros

With the significant enhancement of telecom bandwidth and multimedia-supported mobile devices occupying the market, consuming audiovisual contents on the move is no longer a hype. A lot of telecom operators are now porting traditional TV service to PDAs, 3G cell phones. However, several surveys suggest that direct migration of service from large screen to small screen may not comply with mobile users’ consuming behaviors. In this paper, we first elaborate existing surveys together with our survey result to understand consumer interests in terms of consuming audiovisual contents on mobile devices. Based on the findings, we propose a novel solution to help user locate, gather, and cross-relate topics scattered in an array of contents across various domains. A web-based demo application has been implemented for PDA. The system evaluation indicates that this paradigm of organizing and presenting multimedia archives is very welcomed.

In this paper we focus our attention on solving the contradiction that it is more and more popular to watch videos through mobile devices and there is an explosive growth of mobile devices with multimedia applications but the display sizes of mobile devices are limited and heterogeneous. We present an intact and generic framework to adapt video presentation (AVP). A novel method for choosing the optimal cropped region is introduced to minimize the information loss over adapting video presentation. In order to ameliorate the output stream, we make use of a group of filters for tracking, smoothing and virtual camera controlling. Experiments indicate that our approach is able to achieve satisfactory results and has obvious superiority especially when the display size is pretty small.

Recently, in order to retrieve data objects efficiently according to spatial locations in the spatial main memory DBMS, various multi-dimensional index structures for the main memory have been proposed, which minimize failures in cache access by reducing the entry size. However, because the reduction of entry size requires compression based on the MBR (Minimum Bounding Rectangle) of the parent node or the removal of redundant MBR, the cost of MBR reconstruction increases and the efficiency of search is lowered in index update and search. Thus, to reduce the cost of MBR reconstruction, this paper proposed a RSMBR (Relative-Sized MBR) compression technique, which applies the base point of compression differently in case of broad distribution and narrow distribution. In case of broad distribution, compression is made based on the left-bottom point of the extended MBR of the parent node, and in case of narrow distribution, the whole MBR is divided into cells of the same size and compression is made based on the left-bottom point of each cell. In addition, MBR was compressed using a relative coordinate and the MBR size to reduce the cost of search in index search. Lastly, we evaluated the performance of the proposed RSMBR compression technique using real data, and proved its superiority.

The small screens on increasingly used mobile devices challenge the traditional visualization methods designed for desktops. This paper presents a method called “Radial Edgeless Tree” (RELT) for visualizing trees in a 2-dimensional space. It combines the existing connection tree drawing with the space-filling approach to achieve the efficient display of trees in a small geometrical area, such as the screen that are commonly used in mobile devices. We recursively calculate a set of non-overlapped polygonal nodes that are adjacent in the hierarchical manner. Thus, the display space is fully used for displaying nodes, while the hierarchical relationships among the nodes are presented by the adjacency (or boundary-sharing) of the nodes. It is different from the other traditional connection approaches that use a node-link diagram to present the parent-child relationships which waste the display space. The hierarchy spreads from north-west to south-east in a top-down manner which naturally follows the traditional way of human perception of hierarchies. We discuss the characteristics, advantages and limitations of this new technique and suggestions for future research.

A geographic map is an important browsing tool for multimedia data that can include personal photos, but geographically correct maps are not always easy to use for that purpose due to the frequent zooming and panning, as well as the existence of extraneous information. This paper proposes a new user-interface concept for geo-tagged personal multimedia browsing in the form of a cognitive map. In addition, design criteria are defined and an auto-generation method is presented for this map. The proposed method produces a map represented as a clustered graph with vertices and edges in real time. It is visually compact, preserves geographical relationships among locations and is designed for both PCs and mobile devices. An experiment was conducted to test the proposed method with real-life data sets.

This paper addresses the challenge of automatic annotation of images for semantic image retrieval. In this research, we aim to identify visual features that are suitable for semantic annotation tasks. We propose an image classification system that combines MPEG-7 visual descriptors and support vector machines. The system is applied to annotate cityscape and landscape images. For this task, our analysis shows that the colour structure and edge histogram descriptors perform best, compared to a wide range of MPEG-7 visual descriptors. On a dataset of 7200 landscape and cityscape images representing real-life varied quality and resolution, the MPEG-7 colour structure descriptor and edge histogram descriptor achieve a classification rate of 82.8% and 84.6%, respectively. By combining these two features, we are able to achieve a classification rate of 89.7%. Our results demonstrate that combining salient features can significantly improve classification of images.

In this paper, we introduce a novel high-level visual content descriptor devised for performing semantic-based image classification and retrieval. The work can be treated as an attempt for bridging the so called “semantic gap”. The proposed image feature vector model is fundamentally underpinned by an automatic image labelling framework, called Collaterally Cued Labelling (CCL), which incorporates the collateral knowledge extracted from the collateral texts accompanying the images with the state-of-the-art low-level visual feature extraction techniques for automatically assigning textual keywords to image regions. A subset of the Corel image collection was used for evaluating the proposed method. The experimental results indicate that our semantic-level visual content descriptors outperform both conventional visual and textual image feature models.

Just-in-Time Information Retrieval (JITIR) systems aim to automatically retrieve useful information on the basis of the user’s current task and to present this information to the user without disrupting that task. We ask whether the cognitive mechanism of ‘selective semantic processing’ can minimise the disruptive nature of presenting JITIR information to the user. This mechanism may allow users to subconsciously filter out irrelevant information presented in the periphery of the visual field, while maintaining awareness of relevant information. We report an experiment assessing both attention to peripherally presented information (measured via recall) and level of distraction (measured via typing keystroke rate) in a JITIR system used to write reports on various topics. The experimental results showed that peripherally presented information that was relevant to a user’s writing topic reliably entered their attention significantly more often than irrelevant information, and was significantly less distracting than similar but irrelevant information.

With the explosion of multimedia data especially that of video data, requirement of efficient video retrieval has becoming more and more important. Years of TREC Video Retrieval Evaluation (TRECVID) research gives benchmark for video search task. The video data in TRECVID are mainly news video. In this paper a compound model consisting of several atom search modules, i.e., textual and visual, for news video retrieval is introduced. First, the analysis on query topics helps to improve the performance of video retrieval. Furthermore, the multimodal fusion of all atom search modules ensures to get good performance. Experimental results on TRECVID 2005 and TRECVID 2006 search tasks demonstrate the effectiveness of the proposed method.

This paper describes an ongoing technique to collecting, mining, clustering and visualizing scientific articles and their relations in information science. We aim to provide a valuable tool for researchers in quick analyzing the relationship and retrieving the relevant documents. Our system, called , first automatically searches and retrieves articles from the Internet using given keywords. These articles are next converted into readable text documents. The system next analyzes these documents and it creates similarity matrix. A clustering algorithm is then applied to group the relevant papers into corresponding clusters. Finally, we provide a visual interface so that users can easily view the structure and the citing relations among articles. From the view, they can navigate through the collection as well as retrieve a particular article.