TRECVID: evaluating the effectiveness of information retrieval tasks on digital video

TRECVID is an annual exercise which encourages research in information retrieval from digital video by providing a large video test collection, uniform scoring procedures, and a forum for organizations interested in comparing their results. TRECVID benchmarking covers both interactive and manual searching by end users, as well as the benchmarking of some supporting technologies including shot boundary detection, extraction of some semantic features, and the automatic segmentation of TV news broadcasts into non-overlapping news stories. TRECVID has a broad range of over 40 participating groups from across the world and as it is now (2004) in its 4th annual cycle it is opportune to stand back and look at the lessons we have learned from the cumulative activity. In this paper we shall present a brief and high-level overview of the TRECVID activity covering the data, the benchmarked tasks, the overall results obtained by groups to date and an overview of the approaches taken by selective groups in some tasks. While progress from one year to the next cannot be measured directly because of the changing nature of the video data we have been using, we shall present a summary of the lessons we have learned from TRECVID and include some pointers on what we feel are the most important of these lessons

Extracting corpus specific knowledge bases from Wikipedia

Thesauri are useful knowledge structures for assisting information retrieval. Yet their production is labor-intensive, and few domains have comprehensive thesauri that cover domain-specific concepts and contemporary usage. One approach, which has been attempted without much success for decades, is to seek statistical natural language processing algorithms that work on free text. Instead, we propose to replace costly professional indexers with thousands of dedicated amateur volunteers--namely, those that are producing Wikipedia. This vast, open encyclopedia represents a rich tapestry of topics and semantics and a huge investment of human effort and judgment. We show how this can be directly exploited to provide WikiSauri: manually-defined yet inexpensive thesaurus structures that are specifically tailored to expose the topics, terminology and semantics of individual document collections. We also offer concrete evidence of the effectiveness of WikiSauri for assisting information retrieval

Evaluating the implicit feedback models for adaptive video retrieval

Interactive video retrieval systems are becoming popular. On the one hand, these systems try to reduce the effect of the semantic gap, an issue currently being addressed by the multimedia retrieval community. On the other hand, such systems enhance the quality of information seeking for the user by supporting query formulation and reformulation. Interactive systems are very popular in the textual retrieval domain. However, they are relatively unexplored in the case of multimedia retrieval. The main problem in the development of interactive retrieval systems is the evaluation cost.The traditional evaluation methodology, as used in the information retrieval domain, is not applicable. An alternative is to use a user-centred evaluation methodology. However, such schemes are expensive in terms of effort, cost and are not scalable. This problem gets exacerbated by the use of implicit indicators, which are useful and increasingly used in predicting user intentions. In this paper, we explore the effectiveness of a number of interfaces and feedback mechanisms and compare their relative performance using a simulated evaluation methodology. The results show the relatively better performance of a search interface with the combination of explicit and implicit features

Video information retrieval using objects and ostensive relevance feedback

In this paper, we present a brief overview of current approaches to video information retrieval (IR) and we highlight its limitations and drawbacks in terms of satisfying user needs. We then describe a method of incorporating object-based relevance feedback into video IR which we believe opens up new possibilities for helping users find information in video archives. Following this we describe our own work on shot retrieval from video archives which uses object detection, object-based relevance feedback and a variation of relevance feedback called ostensive RF which is particularly appropriate for this type of retrieval

Personalized Fuzzy Text Search Using Interest Prediction and Word Vectorization

In this paper we study the personalized text search problem. The keyword based search method in conventional algorithms has a low efficiency in understanding users' intention since the semantic meaning, user profile, user interests are not always considered. Firstly, we propose a novel text search algorithm using a inverse filtering mechanism that is very efficient for label based item search. Secondly, we adopt the Bayesian network to implement the user interest prediction for an improved personalized search. According to user input, it searches the related items using keyword information, predicted user interest. Thirdly, the word vectorization is used to discover potential targets according to the semantic meaning. Experimental results show that the proposed search engine has an improved efficiency and accuracy and it can operate on embedded devices with very limited computational resources