Social Media Retrieval Research Articles

Real-time social media retrieval with spatial, temporal and social constraints

Search in social network is continuously being expanded to enhance user experience. Besides basic textual retrieval, users can also search based on features such as spatial proximity, temporal freshness and/or social closeness. To efficiently process each advanced query type, customized indexing mechanisms have been developed. However, such mechanisms only perform well for the query types that they were designed for; moreover, they are not readily adaptable to support other query types. In this paper, we propose an interval-at-a-time (IAAT) framework as a first attempt to provide a one-size-fits-all solution to social media retrieval with spatial, temporal and social constraints. In addition, the algorithm relies on inverted index only, which makes it compatible with conventional search engines. The inverted lists are sorted by document id and the insertion is very fast because only append operation is involved. Experiments conducted on two large-scale Twitter datasets show that though IAAT is a unified strategy, it performs better than most of the state-of-the-art customized solutions in a variety of query types.

Efficient video copy detection using multi-modality and dynamic path search

Efficient and robust video copy detection is an important topic for many applications, such as commercial monitoring and social media retrieval. In this paper, with the aim of handling large-scale video data, we propose an efficient and robust video copy detection method jointly utilizing the characteristics of temporal continuity and multi-modality of video. The video is converted to a continuous sequence of states, and both the visual and auditory features are extracted for temporal frames. To facilitate tolerance of the length variations caused during video re-targeting, an efficient dynamic path search method is proposed to detect the target video clips, and highly compact audio fingerprint and visual ordinal features are jointly utilized in a flexible frame. The proposed scheme not only achieves high computational efficiency but also guarantees effectiveness in real applications. Comparison experiments were conducted using video commercials and real television programs from four channels as well as a benchmark video copy detection dataset, and the results demonstrate both the high efficiency and high robustness of the proposed method.

Exploration on Effectiveness and Efficiency of Similar Sentence Matching

Similar sentence matching is an essential issue for many applications, such as text summarization, image extraction, social media retrieval, question-answer model, and so on. A number of studies have investigated this issue in recent years. Most of such techniques focus on effectiveness issues but only a few focus on efficiency issues. In this paper, we address both effectiveness and efficiency in the sentence similarity matching. For a given sentence collection, we determine how to effectively and efficiently identify the top-k semantically similar sentences to a query. To achieve this goal, we first study several representative sentence similarity measurement strategies, based on which we deliberately choose the optimal ones through cross-validation and dynamically weight tuning. The experimental evaluation demonstrates the effectiveness of our strategy. Moreover, from the efficiency aspect, we introduce several optimization techniques to improve the performance of the similarity computation. The trade-off between the effectiveness and efficiency is further explored by conducting extensive experiments.

Preface for the special issue of MTAP following CBMI 2011

Since the late 90s the Content-BasedMultimedia Indexing (CBMI) has become one of themore active research topics, joining efforts in the computer science (e.g., data mining, information retrieval) and signal processing (e.g., digital image and audio analysis) communities. Since the very beginning, the CBMI community has seen the evolution of research both from a methodological point of view and also from an application point of view. The proliferation of machine-learning approaches in supervised and unsupervised frameworks have witnessed to deal with the phenomenon of large scale in multimedia indexing and retrieval tasks. Content description methods have to make a progress in view of new application areas such as medical images and social media retrieval. Information fusion for efficient mining of multimedia content and also the classical task of information abstraction have tremendous opportunities when applied on multimedia data. The evaluation metrics also will have to progress in the nearest future exploiting the insights gained from the user studies in the field. The International Workshop on Content-based Multimedia Indexing has been established as one of the most important forums for the presentation of the newest ideas and original research results in the field. Following the eight successful previous events of CBMI (Toulouse 1999, Brescia 2001, Rennes 2003, Riga 2005, Bordeaux 2007, London 2008, Chania 2009 and Grenoble 2010), the Video Processing and Understanding Lab (VPULab) and the Information Retrieval Group (IRG) at Universidad Autonoma de Madrid organized the CBMI’2011 event in Madrid from 13 to 15 June 2011. Multimed Tools Appl (2013) 62:1–4 DOI 10.1007/s11042-011-0927-6