Techniques used and open challenges to the analysis, indexing and retrieval of digital video

Smeaton, Alan F.

doi:10.1016/j.is.2006.09.001

Cited by 77 publications

(45 citation statements)

References 12 publications

(13 reference statements)

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“…In the case of shot cuts, the content change is usually large and easier to detect than the content change during a gradual transition (Lienhart, 1999). Many metrics and the classification algorithms have been proposed in the litera-ture during the past decade, e.g., starting with the initial work of (Zhang et al, 1993) (Hampapur et al, 1994) to some recent work of (Smeaton, 2007), (Teng & Tan, 2008). In the following paragraphs of this section, we shall briefly review the metrics used and the classification strategy adopted.…”

Section: Temporal Segmentation Of Digital Videomentioning

confidence: 99%

Video Analysis and Indexing

Ding¹,

Pan²,

Guan³

2010

Digital Video

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Section: Temporal Segmentation Of Digital Videomentioning

confidence: 99%

Video Analysis and Indexing

Ding¹,

Pan²,

Guan³

2010

Digital Video

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“…Content-Based Video Retrieval (CBVR) is concerned about providing users with those videos which satisfy their queries by means of the video content analysis. As a result, the CBVR field has become a very important research area and a wide variety of CBVR systems have been developed [1,2,3,4]. The standard CBVR procedure involves three main components: (i) a query, containing a few video examples of the semantic concept that the user is looking for; (ii) a database, which is used to retrieve videos related to the query concept; and (iii) a ranking function, which sorts the database according to the relevance with respect to the user's query.…”

Section: Introductionmentioning

confidence: 99%

Latent topics-based relevance feedback for video retrieval

Fernández-Beltrán

Pla

2016

Pattern Recognition

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This work presents a novel Content-Based Video Retrieval approach in order to cope with the semantic gap challenge by means of latent topics.Firstly, a supervised topic model is proposed to transform the classical retrieval approach into a class discovery problem. Subsequently, a new probabilistic ranking function is deduced from that model to tackle the semantic gap between low-level features and high-level concepts. Finally, a shortterm relevance feedback scheme is defined where queries can be initialised with samples from inside or outside the database. Several retrieval simulations have been carried out using three databases and seven different ranking functions to test the performance of the presented approach. Experiments revealed that the proposed ranking function is able to provide a competitive advantage within the content-based retrieval field.

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“…[7] As a step toward this goal, we present a unified approach to content-based index-ing and retrieval of digital videos from television archives. The attribute "unified" refers to the ability of the proposed approach to deal with arbitrary television genres, covering all the main steps of a content-based video retrieval system, namely: video segmentation, key-frame extraction, contentbased video indexing and the retrieval operation itself.…”

Section: Introductionmentioning

confidence: 99%

A unified approach to content-based indexing and retrieval of digital videos from television archives

Souza¹,

Pádua

Nunes³

et al. 2014

AIR

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This work addresses the development of a unified approach to content-based indexing and retrieval of digital videos from television archives. The proposed approach has been designed to deal with arbitrary television genres, making it suitable for various applications. To achieve this goal, the main steps of a content-based video retrieval system are addressed in this work, namely: video segmentation, key-frame extraction, content-based video indexing and the video retrieval operation itself. Video segmentation is addressed as a typical TV broadcast structuring problem, which consists in automatically determining the boundaries of each broadcasted program (like movies, news, among others) and inter-program (for instance, commercials). Specifically, to segment the videos, Electronic Program Guide (EPG) metadata is combined with the detection of two special cues, namely, audio cuts (silence) and dark monochrome frames. On the other hand, a color histogram-based approach performs key-frame extraction. Video indexing and retrieval are accomplished by using hashing and k-d tree methods, while visual signatures containing color, shape and texture information are estimated for the key-frames, by using image and frequency domain techniques. Experimental results with the dataset of a multimedia information system especially developed for managing television broadcast archives demonstrate that our approach works efficiently, retrieving videos in 0.16 seconds on average and achieving recall, precision and F1 measure values, as high as 0.76, 0.97 and 0.86 respectively.

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Techniques used and open challenges to the analysis, indexing and retrieval of digital video

Cited by 77 publications

References 12 publications

Video Analysis and Indexing

Video Analysis and Indexing

Latent topics-based relevance feedback for video retrieval

A unified approach to content-based indexing and retrieval of digital videos from television archives

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