Video Corpus Annotation Using Active Learning

Ayache, Stéphane; Quénot, Georges

doi:10.1007/978-3-540-78646-7_19

Cited by 76 publications

(66 citation statements)

References 6 publications

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“…The set of target concepts and the set of training video shots were both large and as a consequence, only a fraction of the training set could be annotated, even using the "crowd" of TRECVid SIN participants and with Quaero support. Also, as most of the target concepts were sparse or very sparse in the training collection (less or much less than 1%), an active learning procedure was used in order to prioritize annotations of the most useful sample shots 9) .…”

Section: Collaborative Annotationmentioning

confidence: 99%

[Invited Paper] TRECVid Semantic Indexing of Video: A 6-Year Retrospective

Awad

Snoek

Smeaton

et al. 2016

MTA

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Semantic indexing, or assigning semantic tags to video samples, is a key component for content-based access to video documents and collections. The Semantic Indexing task has been run at TRECVid from 2010 to 2015 with the support of NIST and the Quaero project. As with the previous High-Level Feature detection task which ran from 2002 to 2009, the semantic indexing task aims at evaluating methods and systems for detecting visual, auditory or multi-modal concepts in video shots. In addition to the main semantic indexing task, four secondary tasks were proposed namely the "localization" task, the "concept pair" task, the "no annotation" task, and the "progress" task. It attracted over 40 research teams during its running period. Both the increase in the volume of video data and in the number of target concepts favored the development of generic and scalable methods. Over 8 millions shots×concepts direct annotations plus over 20 millions indirect ones were produced by the participants and the Quaero project on a total of 800 hours of development data.Significant progress was accomplished during the period as this was accurately measured in the context of the progress task but also from some of the participants' contrast experiments. This paper describes the data, protocol and metrics used for the main and the secondary tasks, the results obtained and the main approaches used by participants.

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Section: Collaborative Annotationmentioning

confidence: 99%

[Invited Paper] TRECVid Semantic Indexing of Video: A 6-Year Retrospective

Awad

Snoek

Smeaton

et al. 2016

MTA

View full text Add to dashboard Cite

show abstract

“…This need of knowledge has raised a new challenge for several competitions such as TRECVID and ImageCLEF Photo Annotation. In this context, various annotation tools have been proposed such as Efficient Video Annotation (EVA) system [52], VideoAnnEx [25], TRECVID 2007 Collaborative Annotation system [3], etc. These annotation tools try to specify with a binary label whether an image contains a given concept or not.…”

Section: Annotation Toolsmentioning

confidence: 99%

A generic framework for semantic video indexing based on visual concepts/contexts detection

Elleuch

Ammar

Alimi

2014

Multimed Tools Appl

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Providing a semantic access to video data requires the development of concept detectors. However, semantic concepts detection is a hard task due to the large intra-class and the small inter-class variability of content. Moreover, semantic concepts co-occur together in various contexts and their occurrence may vary from one to another. Thus, it is interesting to exploit this knowledge in order to achieve satisfactory performances. In this paper we present a generic semantic video indexing scheme, called SVI_REGIMVid. It is based on three levels of analysis. The first level (level1) focuses on low-level processing such as video shot boundary/ key-frame detection, annotation tools, key-points detection and visual features extraction tools. The second level (level2) aims to build the semantic models for supervised learning of concepts/contexts. The third level (level3) enriches the semantic interpretation of concepts/ contexts by exploiting fuzzy knowledge. The obtained experimental results are promising for a semantic concept/context detection process.

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“…Section 5.4.2 describes the kind of retrieval simulations performed in the experiments and the retrieval methods of the literature used to test the proposed approach. Subsequently, sections 4.3.2, 5.4.2 and 5.4.2 show the retrieval results for three different databases: PAL [45], CCV [32] and TRECVID 2007 [7].…”

Section: Methodsmentioning

confidence: 99%

“…Section 5.4 shows the retrieval experiments using three different databases: PAL [45], CCV [32] and TREVID [7]. Finally, Section 5.5 discusses the results and Section 5.6 draws the main conclusions arisen from the work.…”

Section: Objectives and Structurementioning

confidence: 99%

“…Section 5.3 presents the Incremental probabilistic Latent Semantic Analysis (IpLSA) model which is an extension of pLSA in order to reduce computational complexity and to deal with the overfitting problem. In Section 5.4, the experimental setting is described as well as the empirical results obtained by the retrieval systems [23] and [80], including a comparison among pLSA, LDA, FSTM and IpLSA in terms of video retrieval performance using the Consumer Columbia Video database [32] and the collection TRECVID 2007 [7] . Finally, Section 5.5 discusses the results and Section 5.6 draws the main conclusions arising from this chapter.…”

Section: Introductionmentioning

confidence: 99%

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Characterisation and adaptive learning in interactive video retrieval

Beltrán¹

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Retrieving videos by content is a very challenging task because it involves a wide variety of fields. From low-level video descriptors to high-level visual understanding, Content-based Video Retrieval (CBVR) systems have to fill a huge semantic gap to provide users with those videos which satisfy their queries. Even though some of the state-of-the-art approaches have shown to be successful on reduced databases, the ongoing expansion of video collections demands new capabilities in CBVR. Retrieval systems are required to be more efficient to deal with this increasing amount of samples and more effective to cope with more complex query concepts. In this thesis, we explore how difficult this task is and how our contributions try to improve the current state-of-the-art.In this work, we are interested in the use of latent topics to overcome the current limitations in CBVR. Despite the potential of topic models to uncover the hidden structure of a collection, they have traditionally been unable to provide a competitive advantage in CBVR because of the high computational cost of their algorithms and the complexity of the latent space in the visual domain. Throughout this thesis we focus on designing new models and tools based on topic models to take advantage of the latent space in CBVR. Specifically, we have worked in four different areas within the retrieval process: vocabulary reduction, encoding, modelling and ranking, being our most important contributions related to both modelling and ranking.Initially, we present a novel approach to vocabulary reduction based on latent topics in order show how topic models are able to capture the more relevant words of a collection. Subsequently, a new encoding approach specially designed to Content-Based Retrieval tasks is proposed. In the modelling stage, we study how the use of different topic models affects video retrieval performance and present an incremental topic model to cope with incremental scenarios in an effective and efficient way. Regarding the ranking stage, we propose a new proba-3 4 Abstract bilistic ranking function which is deduced from a supervised topic model to tackle the semantic gap between low-level features and high-level concepts through the patterns defined by topics. Finally, we conclude the work with observations on how this investigation has impacted the use of topic models in CBVR. AcknowledgmentsI would like to start by thanking my thesis supervisor Prof. Filiberto Pla for having given me the opportunity to reach this goal and, of course, for having shared with me his brilliant knowledge and ideas. Although obvious, I find it essential to remark that this thesis would have not been possible without him.I would also like to thank all the people who have helped me to shape this work during these years. First, thanks to my colleagues at University Jaume I for your useful comments and suggestions. Thanks as well to my colleagues at Bristol University where I had a short but fruitful research stay. Last but not least, I would like to thank my...

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Video Corpus Annotation Using Active Learning

Cited by 76 publications

References 6 publications

[Invited Paper] TRECVid Semantic Indexing of Video: A 6-Year Retrospective

[Invited Paper] TRECVid Semantic Indexing of Video: A 6-Year Retrospective

A generic framework for semantic video indexing based on visual concepts/contexts detection

Characterisation and adaptive learning in interactive video retrieval

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