Mediaviews: A Layered View Mechanism for Integrating Multimedia Data

Kim, Hyon Hee; Park, Seung Soo

doi:10.1007/978-3-540-45242-3_23

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…Due to the flexibility and self-describing features of our data model, multimedia contents are J OURNAL OF OBJECT TECHNOLOGY V OL. 4, NO. 5…”

Section: Discussionmentioning

confidence: 99%

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A Framework for the Integration of Multimedia Data.

Kim¹,

Park²,

Kim³

2005

JOT

Self Cite

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In this paper, we propose a unified framework, that we call UniMedia, for semanticsbased integration and management of multimedia data. The framework is an adaptation and extension of the federated database technology that has been developed during the past three decades for integrating disparate data sources of alphanumeric data. In particular, we propose a multimedia data model, that we call XML/M, which encompasses diverse types of multimedia data and captures semantic relationships among them. We develop the concept of container objects which cluster relevant multimedia data and customize them according to users' preferences. Further, we augment the concept of container objects with a version management technique. To validate utility of the UniMedia framework, we have implemented a prototype multimedia news management system.

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“…Due to the flexibility and self-describing features of our data model, multimedia contents are J OURNAL OF OBJECT TECHNOLOGY V OL. 4, NO. 5…”

Section: Discussionmentioning

confidence: 99%

“…Container Objects: In our previous paper [4], we designed a mechanism that can be applied to multimedia data. We have extended the mechanism to container objects.…”

Section: The Xml/m Data Modelmentioning

confidence: 99%

A Framework for the Integration of Multimedia Data.

Kim¹,

Park²,

Kim³

2005

JOT

Self Cite

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“…Finally, other promising work aims to simultaneously combine textual and visual features; among these works, there are the OCTOPUS system (Yang et al, 2002) and Mediaviews (Kim and Park, 2003). Current research in the multimedia field is oriented toward the application of data mining methods to extract knowledge from multimedia data sets (Vijayarani and Sakila, 2015).…”

Section: Introductionmentioning

confidence: 99%

Multi-criteria-based fusion for clustering texts and images case study on Flickr

Khatir

Nait-Bahloul

2018

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Purpose This study aims to evaluate a new fusion technique of visual and textual clusters of objects from a real multimedia data-driven collection to improve the performance of multimedia applications. Design/methodology/approach The authors focused on using multi-criteria for clustering texts and images. The algorithm consists of these steps: first is text representation using the statistical method of weighting, second is image representation using a bag of words feature descriptors methods and finally application of multi-criteria clustering. Findings As an application for event detection based on social multimedia data, in particular, Flickr platform. Several experiments were conducted to choose the appropriate parameters for a better scheme of clustering. The new approach achieves better performance when aggregate text clustering is done with image clustering for event detection. Research limitations/implications Further researches would be investigated on other social media platforms such as Facebook and Twitter for a generalization of the technique. Originality/value This study contributes to multimedia data mining through the new fusion technique of clustering. The technique has its root in such strong field as the field of multi-criteria clustering and decision-making support.

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“…So, even though user in not familiar with name of the animal and has an image of it he/she can retrieve information pertaining to that animal. Many earlier cross-media systems [15], [16] focused on integrating and managing heterogeneous multimedia data. In [17], a dual cross-media relevance model is proposed to automatically annotate and retrieve images which considers both text-toimage and text-to-text relations.…”

Section: Introductionmentioning

confidence: 99%

An approach for mining heterogeneous data for cross-media retrieval

Pavan

Ananthanarayana

2013

2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT)

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Due to the wide availability of huge amount of multimedia data in various modalities such as image and text documents, having a great amount of similarity among them is inevitable. In this paper, we present an efficient model which correlates the similarity among documents belonging to various modalities to achieve cross-media retrieval. Cross-media retrieval is a content based information retrieval system where heterogeneous data is mined to retrieve results of various modalities, i.e., input object and returned results may be of different modalities. For example, text objects can be retrieved as a result to image input. First, features are extracted from multimedia objects by which the objects are labeled. Using the labels, similar documents are grouped to generate Multimedia Documents. We construct a cross-media correlation graph with documents as vertices, where positive weight is assigned to every single edge according to the amount of similarity between vertices. The cross-media retrieval system identifies the input document and as a result returns required number of documents with highest weights.

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Mediaviews: A Layered View Mechanism for Integrating Multimedia Data

Cited by 5 publications

References 13 publications

A Framework for the Integration of Multimedia Data.

A Framework for the Integration of Multimedia Data.

Multi-criteria-based fusion for clustering texts and images case study on Flickr

An approach for mining heterogeneous data for cross-media retrieval

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