Dublin City University video track experiments for TREC 2003

In this paper, we describe our experiments for both the News Story Segmentation task and Interactive Search task for TRECVID 2003. Our News Story Segmentation task involved the use of a Support Vector Machine (SVM) to combine evidence from audio-visual analysis tools in order to generate a listing of news stories from a given news programme. Our Search task experiment compared a video retrieval system based on text, image and relevance feedback with a text-only video retrieval system in order to identify which was more effective. In order to do so we developed two variations of our Físchlár video retrieval system and conducted user testing in a controlled lab environment. In this paper we outline our work on both of these two tasks

Using segmented objects in ostensive video shot retrieval

This paper presents a system for video shot retrieval in which shots are retrieved based on matching video objects using a combination of colour, shape and texture. Rather than matching on individual objects, our system supports sets of query objects which in total reflect the user’s object-based information need. Our work also adapts to a shifting user information need by initiating the partitioning of a user’s search into two or more distinct search threads, which can be followed by the user in sequence. This is an automatic process which maps neatly to the ostensive model for information retrieval in that it allows a user to place a virtual checkpoint on their search, explore one thread or aspect of their information need and then return to that checkpoint to then explore an alternative thread. Our system is fully functional and operational and in this paper we illustrate several design decisions we have made in building it

Using video objects and relevance feedback in video retrieval

Video retrieval is mostly based on using text from dialogue and this remains the most signi¯cant component, despite progress in other aspects. One problem with this is when a searcher wants to locate video based on what is appearing in the video rather than what is being spoken about. Alternatives such as automatically-detected features and image-based keyframe matching can be used, though these still need further improvement in quality. One other modality for video retrieval is based on segmenting objects from video and allowing end users to use these as part of querying. This uses similarity between query objects and objects from video, and in theory allows retrieval based on what is actually appearing on-screen. The main hurdles to greater use of this are the overhead of object segmentation on large amounts of video and the issue of whether we can actually achieve effective object-based retrieval. We describe a system to support object-based video retrieval where a user selects example video objects as part of the query. During a search a user builds up a set of these which are matched against objects previously segmented from a video library. This match is based on MPEG-7 Dominant Colour, Shape Compaction and Texture Browsing descriptors. We use a user-driven semi-automated segmentation process to segment the video archive which is very accurate and is faster than conventional video annotation

Personalization via collaboration in web retrieval systems: a context based approach

World Wide Web is a source of information, and searches on the Web can be analyzed to detect patterns in Web users' search behaviors and information needs to effectively handle the users' subsequent needs. The rationale is that the information need of a user at a particular time point occurs in a particular context, and queries are derived from that need. In this paper, we discuss an extension of our personalization approach that was originally developed for a traditional bibliographic retrieval system but has been adapted and extended with a collaborative model for the Web retrieval environment. We start with a brief introduction of our personalization approach in a traditional information retrieval system. Then, based on the differences in the nature of documents, users and search tasks between traditional and Web retrieval environments, we describe our extensions of integrating collaboration in personalization in the Web retrieval environment. The architecture for the extension integrates machine learning techniques for the purpose of better modeling users' search tasks. Finally, a user-oriented evaluation of Web-based adaptive retrieval systems is presented as an important aspect of the overall strategy for personalization

Exploring Topic-based Language Models for Effective Web Information Retrieval

The main obstacle for providing focused search is the relative opaqueness of search request -- searchers tend to express their complex information needs in only a couple of keywords. Our overall aim is to find out if, and how, topic-based language models can lead to more effective web information retrieval. In this paper we explore retrieval performance of a topic-based model that combines topical models with other language models based on cross-entropy. We first define our topical categories and train our topical models on the .GOV2 corpus by building parsimonious language models. We then test the topic-based model on TREC8 small Web data collection for ad-hoc search.Our experimental results show that the topic-based model outperforms the standard language model and parsimonious model

Evidence combination for multi-point query learning in content-based image retrieval

In multipoint query learning a number of query representatives are selected based on the positive feedback samples. The similarity score to a multipoint query is obtained from merging the individual scores. In this paper, we investigate three different combination strategies and present a comparative evaluation of their performance. Results show that the performance of multipoint queries relies heavily on the right choice of settings for the fusion. Unlike previous results, suggesting that multipoint queries generally perform better than a single query representation, our evaluation results do not allow such an overall conclusion. Instead our study points to the type of queries for which query expansion is better suited than a single query, and vice versa

Division of labour and sharing of knowledge for synchronous collaborative information retrieval

Synchronous collaborative information retrieval (SCIR) is concerned with supporting two or more users who search together at the same time in order to satisfy a shared information need. SCIR systems represent a paradigmatic shift in the way we view information retrieval, moving from an individual to a group process and as such the development of novel IR techniques is needed to support this. In this article we present what we believe are two key concepts for the development of effective SCIR namely division of labour (DoL) and sharing of knowledge (SoK). Together these concepts enable coordinated SCIR such that redundancy across group members is reduced whilst enabling each group member to benefit from the discoveries of their collaborators. In this article we outline techniques from state-of-the-art SCIR systems which support these two concepts, primarily through the provision of awareness widgets. We then outline some of our own work into system-mediated techniques for division of labour and sharing of knowledge in SCIR. Finally we conclude with a discussion on some possible future trends for these two coordination techniques