A Survey on the Automatic Indexing of Video Data,

Brunelli, Roberto; Mich, Ornella; Modena, Carla Maria

doi:10.1006/jvci.1997.0404

Cited by 184 publications

(82 citation statements)

References 93 publications

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“…Shot change detection is one of the video processing needed to characterize a video sequence. Brunelli et al [17] also present video indexation, and describe main algorithms including shot change detection. They are particularly involved in a video indexing system.…”

Section: Related Workmentioning

confidence: 99%

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A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval

2003

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We present in this paper a review of methods for segmentation of uncompressed video sequences. Video segmentation is usually performed in the temporal domain by shot change detection. In case of real-time segmentation, computational complexity is one of the criteria which has to be taken into account when comparing different methods. When dealing with uncompressed video sequences, this criterion is even more significant. However previous published reviews did not involve complexity criterion when comparing shot change detection methods. Only recognition rate and ability to classify detected shot changes were considered. So contrary to previous reviews we give here complexity of most of the described methods. We review in this paper an extensive set of methods presented in the literature and classify them in several parts, depending on the information used to detect shot changes. The earliest methods were comparing successive frames by relying on most simple elements, that is to say pixels. Comparison could be performed on a global level, so methods based on histograms were also proposed. Block-based methods have been considered to process data at an intermediate level, between local (using pixels) and global (using histograms) levels. More complex features can be involved, resulting in feature-based methods. Alternatively some methods rely on motion as a criterion to detect shot changes. Finally different kinds of information could be combined together in order to increase the quality of shot change detection. So our review will detail segmentation methods

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Section: Related Workmentioning

confidence: 99%

“…The number of shot change detection methods is now important and several reviews of these methods have been done [3,42,17,7,48,57,44,14,56,22,88,31,32,59]. These reviews often present the different methods and their efficiency based on some quality measures.…”

Section: Introductionmentioning

confidence: 99%

A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval

2003

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“…There are surveys on extracting generic static semantics [151], in which event is an important but distinct subset mentioned in the passing, or on image/video retrieval [20], [118], [129], [131], [155], which can be an application for event analysis. In terms of organization, Section II provides a component overview similar to a few image/video retrieval surveys [78], [129], [131], [155]; Section IV surveys existing event detection systems grouped by common problem scope and solution components, similar to earlier system surveys [20], [118]. This dual structure is necessary since event-modeling problems are very diverse, and there are a number of problem setups and components that the community is actively working on.…”

Section: Introductionmentioning

confidence: 99%

Event Mining in Multimedia Streams

2008

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| Events are real-world occurrences that unfold over space and time. Event mining from multimedia streams improves the access and reuse of large media collections, and it has been an active area of research with notable recent progress. This paper contains a survey on the problems and solutions in event mining, approached from three aspects: event description, event-modeling components, and current event mining systems. We present a general characterization of multimedia events, motivated by the maxim of five BW[s and one BH[ for reporting real-world events in journalism: when, where, who, what, why, and how. We discuss the causes for semantic variability in real-world descriptions, including multilevel event semantics, implicit semantics facets, and the influence of context. We discuss five main aspects of an event detection system. These aspects are: the variants of tasks and event definitions that constrain system design, the media capture setup that collectively define the available data and necessary domain assumptions, the feature extraction step that converts the captured data into perceptually significant numeric or symbolic forms, statistical models that map the feature representations to richer semantic descriptions, and applications that use event metadata to help in different information-seeking tasks. We review current event-mining systems in detail, grouping them by the problem formulations and approaches. The review includes detection of events and actions in one or more continuous sequences, events in edited video streams, unsupervised event discovery, events in a collection of media objects, and a discussion on ongoing benchmark activities. These problems span a wide range of multimedia domains such as surveillance, meetings, broadcast news, sports, documentary, and films, as well as personal and online media collections. We conclude this survey with a brief outlook on open research directions.

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“…One can accomplish this task by using explicit handcrafted and thus domain-dependent models which can perform reasonably well in some cases [2]. But soon it was realized that we need more effective ways to bridge the required high-level user needs and the low-level video features we have at hand, such as image histograms or speaker excitation.…”

Section: Introductionmentioning

confidence: 99%

Audiovisual fusion with segment models for video structure analysis

Delakis¹,

Gravier²,

Gros³

2005

2nd European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology (EWIMT 2005)

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Hidden Markov Models provide a powerful framework for bridging the semantic gap between low-level video features and high-level user needs by taking full advantage of our prior knowledge on the video structure. A serious flaw of HMMs is that they require all the modalities of a video document to be strictly synchronous before their fusion. Taking as a case study tennis broadcasts analysis, we introduce video indexing using Segment Models, a generalization of Hidden Markov Models, where the fusion of different modalities can be performed in a more flexible way. Operating essentially as a layered topology they allow the fusion of asynchronous modalities but do not rely on synchronization points fixed a priori. They also facilitate the fusion of audio models of high-level semantics, like the content of a complete scene, on top of the raw lowlevel audio frames. Segment Models provide encouraging experimental results.

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A Survey on the Automatic Indexing of Video Data,

Cited by 184 publications

References 93 publications

A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval

A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval

Event Mining in Multimedia Streams

Audiovisual fusion with segment models for video structure analysis

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