Design, implementation and testing of an interactive video retrieval system

Gaughan, Georgina; Smeaton, Alan F.; Gurrin, Cathal; Lee, Hyowon; McDonald, Kieran

doi:10.1145/973264.973269

Cited by 23 publications

(13 citation statements)

References 9 publications

(1 reference statement)

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“…The video retrieval system proposed by Amir et al [1] performs a query-independent linear combination on both text/image retrieval systems, where the per-modality weights are chosen to maximize mean average precision score on development data. Gaughan et al [8] ranks the video shots based on the summation of feature scores and automatic speech retrieval scores, where the influence of speech retrieval is at 4 times that of any other features. Benitez et al [2] proposed content-based meta-search image search engine, called Metaseek.…”

Section: Related Workmentioning

confidence: 99%

Learning query-class dependent weights in automatic video retrieval

Yan

Yang

Hauptmann

2004

Proceedings of the 12th Annual ACM International Conference on Multimedia

108

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Combining retrieval results from multiple modalities plays a crucial role for video retrieval systems, especially for automatic video retrieval systems without any user feedback and query expansion. However, most of current systems only utilize query independent combination or rely on explicit user weighting. In this work, we propose using query-class dependent weights within a hierarchial mixture-of-expert framework to combine multiple retrieval results. We first classify each user query into one of the four predefined categories and then aggregate the retrieval results with query-class associated weights, which can be learned from the development data efficiently and generalized to the unseen queries easily. Our experimental results demonstrate that the performance with query-class dependent weights can considerably surpass that with the query independent weights.

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Section: Related Workmentioning

confidence: 99%

Learning query-class dependent weights in automatic video retrieval

Yan

Yang

Hauptmann

2004

Proceedings of the 12th Annual ACM International Conference on Multimedia

108

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“…Here we seek to organise broadcast news retrieval results based on the degree of "newness" to the topic rather than the traditional ranking by degree of relevance, thereby reducing a user's time to locate new and interesting content. Within our group, we have much experience of developing Interactive Video Retrieval Systems [4]. Leveraging this experience and our preliminary experiments described above, we are in the process of developing a novel video retrieval system which uses more than just text from spoken dialogue.…”

Section: Novelty In Videomentioning

confidence: 99%

Finding New News: Novelty Detection in Broadcast News

Gaughan

Smeaton

2005

Information Retrieval Technology

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Abstract. The automatic detection of novelty, or newness, as part of an information retrieval system would greatly improve a searcher's experience by presenting "documents" in order of how much extra information they add to what is already known instead of how similar they are to a user's query. In this paper we present a novelty detection system evaluated on the AQUAINT text collection as part of our TREC 2004 Novelty Track experiments. Subsequent to participation in TREC, the algorithm has been evaluated on another collection with its parameters optimized and we present those results here. We also discuss how we are extending the text-only approach to novelty detection to also include input from video analysis.

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“…While such a model is very well suited to represent a single multimedia document, it cannot be used efficiently for accessing, querying and managing a large collection of such documents due to its inherent complexity. Unfortunately, most video retrieval systems presented in state of the art literature [1,4] do not explicitely discuss the way they address such management issues.…”

Section: Motivationsmentioning

confidence: 99%

Managing video collections at large

Moenne-Loccoz¹,

Janvier²,

Marchand-Maillet³

et al. 2004

Proceedings of the 1st International Workshop on Computer Vision Meets Databases

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Video document retrieval is now an active part of the domain of multimedia retrieval. However, unlike for other media, the management of a collection of video documents adds the problem of efficiently handling an overwhelming volume of temporal data. Challenges include balancing efficient content modeling and storage against fast access at various levels. In this paper, we detail the framework we have built to accommodate our developments in content-based multimedia retrieval. We show that not only our framework facilitates the developments of processing and indexing algorithms but it also opens the way to several other possibilities such as rapid interface prototyping or retrieval algorithms benchmarking. In this respect, we discuss our developments in relation to wider contexts such as MPEG-7 and the TREC Video Track. MOTIVATIONSVideo data processing has for long been of high interest for the development of compression and efficient transmission algorithms. In parallel, the domain of content-based multimedia retrieval has developed, initially from text retrieval, then for images and now addressing video content retrieval. Whereas in text and image retrieval the volume of data and associated access techniques are well under control, this is largely not the case for video collection management. Not only video data volume may rapidly grow complex and huge but it also requires efficient access techniques associated to the temporal aspect of the data.Efforts in video content modeling such as MPEG-7 [9] are providing a base for the solution to the problem of handling large amount of multimedia data. While such a model is very well suited to represent a single multimedia document, it cannot be used efficiently for accessing, querying and managing a large collection of such documents due to its inherent complexity. Unfortunately, most video retrieval systems presented in state of the art literature [1, 4] do not explicitely discuss the way they address such management Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. CVDB '04 Paris, France Copyright 2004 ACM 1-58113-917-9/04/06 ...$5.00. issues.In this paper, we detail the framework we have constructed for the management of video document collections in the context of our research in video content analysis. Rather than presenting a temporal document model alone, our ultimate goal is to develop content characterization and indexing algorithms for the management of large video collections. When addressing such problems, one rapidly faces the need for a favorable context on which to base these developments and also that permits rapid and objective evaluation of research findings. From an extensible multimedia...

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Design, implementation and testing of an interactive video retrieval system

Cited by 23 publications

References 9 publications

Learning query-class dependent weights in automatic video retrieval

Learning query-class dependent weights in automatic video retrieval

Finding New News: Novelty Detection in Broadcast News

Managing video collections at large

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