K-Space Interactive Search

In this paper we will present the K-Space1 Interactive Search system for content-based video information retrieval to be demonstrated in the VideOlympics. This system is an exten-sion of the system we developed as part of our participation in TRECVID 2007 [1]. In TRECVID 2007 we created two interfaces, known as the ‘Shot’ based and ‘Broadcast’ based interfaces. Our VideOlympics submission takes these two in-terfaces and the lessons learned from our user experiments, to create a single user interface which attempts to leverage the best aspects of both

A Methodology for Simulated Experiments in Interactive Search

Interactive information retrieval has received much attention in recent years, e.g. [7]. Furthermore, increased activity in developing interactive features in search systems used across existing popular Web search engines suggests that interactive systems are being recognised as a promising next step in assisting information search. One of the most challenging problems with interactive systems however remains evaluation. We describe the general specifications of a methodology for conducting controlled and reproducible experiments in the context of interactive search. It was developed in the AutoAdapt project1 focusing on search in intranets, but the methodology is more generic than that and can be applied to interactive Web search as well. The goal of this methodology is to evaluate the ability of different algorithms to produce domain models that provide accurate suggestions for query modifications. The AutoAdapt project investigates the application of automatically constructed adaptive domain models for providing suggestions for query modifications to the users of an intranet search engine. This goes beyond static models such as the one employed to guide users who search the Web site of the University of Essex which is based on a domain model that has been built in advance using the documents’ markup structure

Diversity, Assortment, Dissimilarity, Variety: A Study of Diversity Measures Using Low Level Features for Video Retrieval

In this paper we present a number of methods for re-ranking video search results in order to introduce diversity into the set of search results. The usefulness of these approaches is evaluated in comparison with similarity based measures, for the TRECVID 2007 collection and tasks [11]. For the MAP of the search results we find that some of our approaches perform as well as similarity based methods. We also find that some of these results can improve the P@N values for some of the lower N values. The most successful of these approaches was then implemented in an interactive search system for the TRECVID 2008 interactive search tasks. The responses from the users indicate that they find the more diverse search results extremely useful

Dublin City University video track experiments for TREC 2001

Dublin City University participated in the interactive search task and Shot Boundary Detection task* of the TREC Video Track. In the interactive search task experiment thirty people used three different digital video browsers to find video segments matching the given topics. Each user was under a time constraint of six minutes for each topic assigned to them. The purpose of this experiment was to compare video browsers and so a method was developed for combining independent users’ results for a topic into one set of results. Collated results based on thirty users are available herein though individual users’ and browsers’ results are currently unavailable for comparison. Our purpose in participating in this TREC track was to create the ground truth within the TREC framework, which will allow us to do direct browser performance comparisons

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

Interactive Search and Exploration in Online Discussion Forums Using Multimodal Embeddings

In this paper we present a novel interactive multimodal learning system, which facilitates search and exploration in large networks of social multimedia users. It allows the analyst to identify and select users of interest, and to find similar users in an interactive learning setting. Our approach is based on novel multimodal representations of users, words and concepts, which we simultaneously learn by deploying a general-purpose neural embedding model. We show these representations to be useful not only for categorizing users, but also for automatically generating user and community profiles. Inspired by traditional summarization approaches, we create the profiles by selecting diverse and representative content from all available modalities, i.e. the text, image and user modality. The usefulness of the approach is evaluated using artificial actors, which simulate user behavior in a relevance feedback scenario. Multiple experiments were conducted in order to evaluate the quality of our multimodal representations, to compare different embedding strategies, and to determine the importance of different modalities. We demonstrate the capabilities of the proposed approach on two different multimedia collections originating from the violent online extremism forum Stormfront and the microblogging platform Twitter, which are particularly interesting due to the high semantic level of the discussions they feature

K-Space at TRECVid 2007

In this paper we describe K-Space participation in TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance. The first of the two systems was a ‘shot’ based interface, where the results from a query were presented as a ranked list of shots. The second interface was ‘broadcast’ based, where results were presented as a ranked list of broadcasts. Both systems made use of the outputs of our high-level feature submission as well as low-level visual features

BIRP: Software for interactive search and retrieval of image engineering data

Better Image Retrieval Programs (BIRP), a set of programs to interactively sort through and to display a database, such as engineering data for images acquired by spacecraft is described. An overview of the philosophy of BIRP design, the structure of BIRP data files, and examples that illustrate the capabilities of the software are provided

A strategy for evaluating search of “Real” personal information archives

Personal information archives (PIAs) can include materials from many sources, e.g. desktop and laptop computers, mobile phones, etc. Evaluation of personal search over these collections is problematic for reasons relating to the personal and private nature of the data and associated information needs and measuring system response effectiveness. Conventional information retrieval (IR) evaluation involving use of Cranfield type test collections to establish retrieval effectiveness and laboratory testing of interactive search behaviour have to be re-thought in this situation. One key issue is that personal data and information needs are very different to search of more public third party datasets used in most existing evaluations. Related to this, understanding the issues of how users interact with a search system for their personal data is important in developing search in this area on a well grounded basis. In this proposal we suggest an alternative IR evaluation strategy which preserves privacy of user data and enables evaluation of both the accuracy of search and exploration of interactive search behaviour. The general strategy is that instead of a common search dataset being distributed to participants, we suggest distributing standard expandable personal data collection, indexing and search tools to non-intrusively collect data from participants conducting search tasks over their own data collections on their own machines, and then performing local evaluation of individual results before central agregation