A Rule-Based Video Annotation System

Dorado, A.; Calic, Janko; Izquierdo, Ebroul

doi:10.1109/tcsvt.2004.826764

Cited by 37 publications

(10 citation statements)

References 17 publications

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“…Towards this end, we apply image tagging techniques on video frames to generate an appropriate description of the content of video scenes [12,34]. Existing approaches for video tagging [14,27,42] are usually able to achieve satisfying levels of accuracy only for specific domains or for short video sequences. Indeed, videos included in large general purpose collections usually are several minutes long and have a heterogeneous content, making it hard to automatically derive significant labels for such videos.…”

Section: Fig 1 Tagging Videos Using Textual Labelsmentioning

confidence: 99%

“…The task of automatic shot tagging is commonly carried out either by exploiting a knowledge restricted to a domain [14,27] or by applying similarity search principles that have a more general applicability [4,42]. Since the former is usually based on specific descriptors and/or on machine learning tools, their validity is limited to the existence of a very specific understanding of the underlying domain, which is clearly not the case for general purpose systems, like those commonly used by media producers (e.g., TV broadcasters).…”

Section: Related Workmentioning

confidence: 99%

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SHIATSU: tagging and retrieving videos without worries

Bartolini

Patella

Romani

2011

Multimed Tools Appl

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The dramatic growth of video content over modern media channels (such as the Internet and mobile phone platforms) directs the interest of media broadcasters towards the topics of video retrieval and content browsing. Several video retrieval systems benefit from the use of semantic indexing based on content, since it allows an intuitive categorization of videos. However, indexing is usually performed through manual annotation, thus introducing potential problems such as ambiguity, lack of information, and non-relevance of index terms. In this paper, we present SHIATSU, a complete system for video retrieval which is based on the (semi-)automatic hierarchical semantic annotation of videos exploiting the analysis of visual content; videos can then be searched by means of attached tags and/or visual features. We experimentally evaluate the performance of SHIATSU on two different real video benchmarks, proving its accuracy and efficiency.

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Section: Fig 1 Tagging Videos Using Textual Labelsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

SHIATSU: tagging and retrieving videos without worries

Bartolini

Patella

Romani

2011

Multimed Tools Appl

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“…In [14], decision trees were used to infer high-level concepts from low-level video visual features. Also in [3] a rule learning process, depending on both low-level and middlelevel features, build a decision tree to mark rare events and concepts.…”

Section: Learningmentioning

confidence: 99%

Automatic semantic video annotation in wide domain videos based on similarity and commonsense knowledgebases

Altadmri

Ahmed

2009

2009 IEEE International Conference on Signal and Image Processing Applications

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Abstract-In this paper, we introduce a novel framework for automatic Semantic Video Annotation. As this framework detects possible events occurring in video clips, it forms the annotating base of video search engine. To achieve this purpose, the system has to able to operate on uncontrolled wide-domain videos. Thus, all layers have to be based on generic features.This framework aims to bridge the "semantic gap", which is the difference between the low-level visual features and the human's perception, by finding videos with similar visual events, then analyzing their free text annotation to find a common area then to decide the best description for this new video using commonsense knowledgebases.Experiments were performed on wide-domain video clips from the TRECVID 2005 BBC rush standard database. Results from these experiments show promising integrity between those two layers in order to find expressing annotations for the input video. These results were evaluated based on retrieval performance.

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“…In [1] temporal segmentation techniques are reviewed in a general terms. Several interrelated applications such as content based image retrieval [2], video annotation [3], video indexing [4] has been a major focus of research. Most of these applications are based on low level processing of pixel data.…”

Section: Introductionmentioning

confidence: 99%

A Case Study on Logging Visual Activities: Chess Game

Ozan

Gumustekin

2006

Artificial Intelligence and Neural Networks

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Abstract. Automatically recognizing and analyzing visual activities in complex environments is a challenging and open-ended problem. In this study this task is performed in a chess game scenario where the rules, actions and the environment are well defined. The purpose here is to detect and observe a FIDE (Fédération International des Ėchecs) compatible chess board, generating a log file of the moves made by human players. A series of basic image processing operations have been applied to perform the desired task. The first step of automatically detecting a chess board is followed by locating the positions of the pieces. After the initial setup is established every move made by a player is automatically detected and verified. Intel® Open Source Computer Vision Library (OpenCV) is used in the current software implementation.

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A Rule-Based Video Annotation System

Cited by 37 publications

References 17 publications

SHIATSU: tagging and retrieving videos without worries

SHIATSU: tagging and retrieving videos without worries

Automatic semantic video annotation in wide domain videos based on similarity and commonsense knowledgebases

A Case Study on Logging Visual Activities: Chess Game

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