CORE: a content-based retrieval engine for multimedia information systems

Wu, Jiankang; Narasimhalu, Arcot Desai; Mehtre, Babu M.; Lam, C. P.; Gao, Y. J.

doi:10.1007/bf01236577

Cited by 114 publications

(44 citation statements)

References 22 publications

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“…For completeness of the discussion, let us start from the multimedia object definition in [5] as follows: Multimedia Object (MOB) can be defined using a six-tuple…”

Section: Content-based Retrievalmentioning

confidence: 99%

“…Pentland et al [3] described a photobook system, which is a set of interactive tools for browsing and searching images for face, shape, and texture. Wu and his colleagues have developed a content-based retrieval engine [5] and used it to build a facial image retrieval system for criminal identification [6] and a system for trademark archival and registration [7].…”

Section: Introductionmentioning

confidence: 99%

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Content-based indexing of multimedia databases

1997

IEEE Trans. Knowl. Data Eng.

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Abstract-Content-based retrieval of multimedia database calls for content-based indexing techniques. Different from conventional databases, where data items are represented by a set of attributes of elementary data types, multimedia objects in multimedia databases are represented by a collection of features; similarity of object contents depends on context and frame of reference; and features of objects are characterized by multimodal feature measures. These lead to great challenges for content-based indexing. On the other hand, there are special requirements on content-based indexing: To support visual browsing, similarity retrieval, and fuzzy retrieval, nodes of the index should represent certain meaningful categories.

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“…For completeness of the discussion, let us start from the multimedia object definition in [5] as follows: Multimedia Object (MOB) can be defined using a six-tuple…”

Section: Content-based Retrievalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Content-based indexing of multimedia databases

1997

IEEE Trans. Knowl. Data Eng.

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“…Retrieval is also referred to as content retrieval. There have been many reports on research related to audio and video content retrieval [1][2][3][4][5][6][7]. On the other hand, a search refers to finding out where specific sounds and videos (reference signals) are located in a database or lengthy materials (input signal).…”

Section: Introductionmentioning

confidence: 99%

A quick AND/OR search for multimedia signals based on histogram features

Kashino¹,

Kitasaka²,

Murase³

2003

Electron Comm Jpn Pt III

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SUMMARYThe problem of finding the point in time at which a known audio or video source (reference signal) appears in a long audio or video source (input signal) is referred to as a time series search, in contrast to a text string search. In a time series search, a search can be performed quickly by combining the audio and video, and then identifying several search conditions using logical equations. Thus, in this paper the authors describe the application of the time series active search method, a method to search audio signals proposed in the authors' previous paper, to video searches. Next, the authors propose an efficient algorithm for AND searches and OR searches of reference signals. In addition, the authors propose a multimodal AND search which combines audio and video. The proposed algorithms are faster than combining the results of searches performed individually. For instance, in an OR search of a reference signal, when the mutual similarity in the reference signals is above 0.8, five reference signals can be searched in a search time that is 1.2 times faster than searching one reference signal.

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“…The audio and visual information in the video could be effectively employed for development of semantic indexing and retrieval systems [1] and has been an attractive research area for over two decades now [2], [3]. In some cases, especially on the video sharing portals, users manually assign tags to videos allowing their retrieval.…”

Section: Introductionmentioning

confidence: 99%

Multilingual Artificial Text Extraction and Script Identification from Video Images

Jamıl¹,

Batool²,

Malik³

et al. 2016

ijacsa

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Abstract-This work presents a system for extraction and script identification of multilingual artificial text appearing in video images. As opposed to most of the existing text extraction systems which target textual occurrences in a particular script or language, we have proposed a generic multilingual text extraction system that relies on a combination of unsupervised and supervised techniques. The unsupervised approach is based on application of image analysis techniques which exploit the contrast, alignment and geometrical properties of text and identify candidate text regions in an image. Potential text regions are then validated by an Artificial Neural Network (ANN) using a set of features computed from Gray Level Co-occurrence Matrices (GLCM). The script of the extracted text is finally identified using texture features based on Local Binary Patterns (LBP). The proposed system was evaluated on video images containing textual occurrences in five different languages including English, Urdu, Hindi, Chinese and Arabic. The promising results of the experimental evaluations validate the effectiveness of the proposed system for text extraction and script identification.

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CORE: a content-based retrieval engine for multimedia information systems

Cited by 114 publications

References 22 publications

Content-based indexing of multimedia databases

Content-based indexing of multimedia databases

A quick AND/OR search for multimedia signals based on histogram features

Multilingual Artificial Text Extraction and Script Identification from Video Images

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