Content‐based video retrieval: Three example systems from TRECVid

Smeaton, Alan F.; Wilkins, Peter; Worring, Marcel; Rooij, O. de; Chua, Tat-Seng; Luan, Huanbo

doi:10.1002/ima.20150

Cited by 19 publications

(15 citation statements)

References 14 publications

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“…CHIIR '17 March 07-11, 2017, Oslo, Norway of finding the proper scene of interest in a large video archive (e.g., [1,4,5,6,11,16,17,20,22,25,15]). Video retrieval tools use content-based indexing methods to perform automatic annotation with content descriptors for color, texture, shape, and semantic concepts.…”

Section: Interactive Video Searchmentioning

confidence: 99%

Interactive Search in Video & Lifelogging Repositories

Hopfgartner

Schoeffmann

2017

Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval

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Due to increasing possibilities to create digital video, we are facing the emergence of large video archives that are made accessible either online or offline. Though a lot of research has been spent on video retrieval tools and methods, which allow for automatic search in videos, still the performance of automatic video retrieval is far from optimal.At the same time, the organization of personal data is receiving increasing research attention due to the challenges that are faced in gathering, enriching, searching and visualizing this data. Given the increasing quantities of personal data being gathered by individuals, the concept of a heterogeneous personal digital libraries of rich multimedia and sensory content for every individual is becoming a reality.Despite the differences between video archives and personal lifelogging libraries, we are facing very similar challenges when accessing these multimedia repositories. For example, users will struggle to find the information they are looking for in either collection if they are not able to formulate their search needs through a query. In this tutorial we discussed (i) proposed solutions for improved video & lifelog content navigation, (ii) typical interaction of content-based querying features, and (iii) advanced content visualization methods. Moreover, we discussed and demonstrate interactive video & lifelog search systems and ways to evaluate their performance.

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Section: Interactive Video Searchmentioning

confidence: 99%

Interactive Search in Video & Lifelogging Repositories

Hopfgartner

Schoeffmann

2017

Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval

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“…B V Patel et al [16] have proposed a method for development of multimedia data types and available bandwidth there was huge demand of video retrieval systems, as users shift from text based retrieval systems to content based retrieval systems. Selection of extracted features play an important role in content based video retrieval regardless of video attributes being under consideration.…”

Section: Review On Related Researchesmentioning

confidence: 99%

A Content Based Video Retrieval Analysis System with Extensive Features by Using Kullback-Leibler

Priya¹,

Shanmugam²,

Baskaran³

2014

IJCIS

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Content-based video retrieval systems have shown great potential in supporting decision making in clinical activities, teaching, and biological research. In content-based video retrieval, feature combination plays a key role. As a result content-based retrieval of all different type video data turns out to be a challenging and vigorous problem. This paper presents an effective content based video retrieval system, which recognizes and retrieves videos with three different types of visual effects. The raw video information is divided into shots and also the object feature, movement feature and also the occlusion options are extracted from these shots and also the feature library is used for the storage method of those options. Advanced on, the Kullback-Leibler distance is computed among the options of the feature library and also the options of the question clip that's extracted within the similar manner. The results show that it is possible to improve a system for content-based video retrieval by using Kullback-Leibler distance model, which takes careful consideration of the structure and distribution of visual features. Hence the final results with the aid of the Kullback-Leibler distance the similar videos are extracted from the collection of videos based on the given query video clip in an effective manner.

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“…Even when there are high-level classifiers available, the literature has shown that combining low-and high-level features can achieve best performance [11,21]. Furthermore, low-level features are more scalable than high-level features, as they can be exploited without requiring a training collection.…”

Section: Introductionmentioning

confidence: 99%

Exploiting semantics on external resources to gather visual examples for video retrieval

Vallet

Cantador

Jose

2012

Int J Multimed Info Retr

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With the huge and ever rising amount of video content available on the Web, there is a need to facilitate video retrieval functionalities on very large collections. Most of the current Web video retrieval systems rely on manual textual annotations to provide keyword-based search interfaces. These systems have to face the problems that users are often reticent to provide annotations, and that the quality of such annotations is questionable in many cases. An alternative commonly used approach is to ask the user for an image example, and exploit the low-level features of the image to find video content whose keyframes are similar to the image. In this case, the main limitation is the so-called semantic gap, which consists of the fact that low-level image features often do not match with the real semantics of the videos. Moreover, this approach may be a burden to the user, as it requires finding and providing the system with relevant visual examples. Aiming to address this limitation, in this paper, we present a hybrid video retrieval technique that automatically obtains visual examples by performing textual searches on external knowledge sources, such as DBpedia, Flickr and Google Images, which have different coverage and structure characteristics. Our approach exploits the semantics underlying the above knowledge sources to address the semantic gap problem. We have conducted evaluations to assess the quality of visual examples retrieved from the above external knowledge sources.

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Content‐based video retrieval: Three example systems from TRECVid

Cited by 19 publications

References 14 publications

Interactive Search in Video & Lifelogging Repositories

Interactive Search in Video & Lifelogging Repositories

A Content Based Video Retrieval Analysis System with Extensive Features by Using Kullback-Leibler

Exploiting semantics on external resources to gather visual examples for video retrieval

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