Comparison of Balancing Techniques for Multimedia IR over Imbalanced Datasets

A promising method to improve the performance of information retrieval systems is to approach retrieval tasks as a supervised classification problem. Previous user interactions, e.g. gathered from a thorough log file analysis, can be used to train classifiers which aim to inference relevance of retrieved documents based on user interactions. A problem in this approach is, however, the large imbalance ratio between relevant and non-relevant documents in the collection. In standard test collection as used in academic evaluation frameworks such as TREC, non-relevant documents outnumber relevant documents by far. In this work, we address this imbalance problem in the multimedia domain. We focus on the logs of two multimedia user studies which are highly imbalanced. We compare a naiinodotve solution of randomly deleting documents belonging to the majority class with various balancing algorithms coming from different fields: data classification and text classification. Our experiments indicate that all algorithms improve the classification performance of just deleting at random from the dominant class

Simulated testing of an adaptive multimedia information retrieval system

The Semantic Gap is considered to be a bottleneck in image and video retrieval. One way to increase the communication between user and system is to take advantage of the user's action with a system, e.g. to infer the relevance or otherwise of a video shot viewed by the user. In this paper we introduce a novel video retrieval system and propose a model of implicit information for interpreting the user's actions with the interface. The assumptions on which this model was created are then analysed in an experiment using simulated users based on relevance judgements to compare results of explicit and implicit retrieval cycles. Our model seems to enhance retrieval results. Results are presented and discussed in the final section

Video browsing interfaces and applications: a review

We present a comprehensive review of the state of the art in video browsing and retrieval systems, with special emphasis on interfaces and applications. There has been a significant increase in activity (e.g., storage, retrieval, and sharing) employing video data in the past decade, both for personal and professional use. The ever-growing amount of video content available for human consumption and the inherent characteristics of video data—which, if presented in its raw format, is rather unwieldy and costly—have become driving forces for the development of more effective solutions to present video contents and allow rich user interaction. As a result, there are many contemporary research efforts toward developing better video browsing solutions, which we summarize. We review more than 40 different video browsing and retrieval interfaces and classify them into three groups: applications that use video-player-like interaction, video retrieval applications, and browsing solutions based on video surrogates. For each category, we present a summary of existing work, highlight the technical aspects of each solution, and compare them against each other

TV News Story Segmentation Based on Semantic Coherence and Content Similarity

In this paper, we introduce and evaluate two novel approaches, one using video stream and the other using close-caption text stream, for segmenting TV news into stories. The segmentation of the video stream into stories is achieved by detecting anchor person shots and the text stream is segmented into stories using a Latent Dirichlet Allocation (LDA) based approach. The benefit of the proposed LDA based approach is that along with the story segmentation it also provides the topic distribution associated with each segment. We evaluated our techniques on the TRECVid 2003 benchmark database and found that though the individual systems give comparable results, a combination of the outputs of the two systems gives a significant improvement over the performance of the individual systems

Exploiting log files in video retrieval

While research into user-centered text retrieval is based on mature evaluation methodologies, user evaluation in multimedia retrieval is still in its infancy. User evaluations can be expensive and are also often non-repeatable. An alternative way of evaluating such systems is the use of simulations. In this poster, we present an evaluation methodology which is based on exploiting log files recorded from a user-study we conducted

Semantic User Modelling for Personal News Video Retrieval

There is a need for personalised news video retrieval due to the explosion of news materials available through broadcast and other channels. In this work we introduce a semantic based user modeling technique to capture the users’ evolving information needs. Our approach exploits the Linked Open Data Cloud to capture and organise users’ interests. The organised interests are used to retrieve and recommend news stories to users. The system monitors user interaction with its interface and uses this information for capturing their evolving interests in the news. New relevant materials are fetched and presented to the user based on their interests. A user-centred evaluation was conducted and the results show the promise of our approach

Glasgow University at TRECVID 2006

In the first part of this paper we describe our experiments in the automatic and interactive search tasks of TRECVID 2006. We submitted five fully automatic runs, including a text baseline, two runs based on visual features, and two runs that combine textual and visual features in a graph model. For the interactive search, we have implemented a new video search interface with relevance feedback facilities, based on both textual and visual features. The second part is concerned with our approach to the high-level feature extraction task, based on textual information extracted from speech recogniser and machine translation outputs. They were aligned with shots and associated with high-level feature references. A list of significant words was created for each feature, and it was in turn utilised for identification of a feature during the evaluation

Real-time 2D separation by LC × differential ion mobility hyphenated to mass spectrometry

The liquid chromatography-mass spectrometry (LC-MS) analysis of complex samples such as biological fluid extracts is widespread when searching for new biomarkers as in metabolomics. The success of this hyphenation resides in the orthogonality of both separation techniques. However, there are frequent cases where compounds are co-eluting and the resolving power of mass spectrometry (MS) is not sufficient (e.g., isobaric compounds and interfering isotopic clusters). Different strategies are discussed to solve these cases and a mixture of eight compounds (i.e., bromazepam, chlorprothixene, clonapzepam, fendiline, flusilazol, oxfendazole, oxycodone, and pamaquine) with identical nominal mass (i.e., m/z 316) is taken to illustrate them. Among the different approaches, high-resolution mass spectrometry or liquid chromatography (i.e., UHPLC) can easily separate these compounds. Another technique, mostly used with low resolving power MS analyzers, is differential ion mobility spectrometry (DMS), where analytes are gas-phase separated according to their size-to-charge ratio. Detailed investigations of the addition of different polar modifiers (i.e., methanol, ethanol, and isopropanol) into the transport gas (nitrogen) to enhance the peak capacity of the technique were carried out. Finally, a complex urine sample fortified with 36 compounds of various chemical properties was analyzed by real-time 2D separation LC×DMS-MS(/MS). The addition of this orthogonal gas-phase separation technique in the LC-MS(/MS) hyphenation greatly improved data quality by resolving composite MS/MS spectra, which is mandatory in metabolomics when performing database generation and searc

Digital preservation and curation of self-tracking data: A position paper

Thanks to recent advances in the field of ubiquitous computing, an increasing number of users now rely on tools and apps that allow them to track specific aspects of their lives. An example are step counters and activity trackers that are promoted as unobtrusive tools to monitor our fitness levels. Interestingly, although significant research and development efforts went into improving the accuracy of these self-tracking devices, hardly any research is performed on the digital preservation of the data created. This position paper highlights challenges and opportunities arising from the digital preservation of self-tracking data