• Journal of Multimedia Information System
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• v.6 no.4
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• pp.197-202
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• 2019

Society advances through technology, and technology has changed many lifestyles. The need for food is varying, but the availability of food is constantly changing as trends in production change. Combining the food industry and technology, a robot that delivers food and also cooks it has been developed. The time has come for a combination of food content and technology to advance the restaurant industry. This study discusses the application of a recommended food content media providing system using a curation engine that recommends contents according to individual tastes and preferences for the convenience of those who use food contents, using artificial intelligence speakers. We discuss the technologies required to develop video contents optimized for AI speakers with screens and shapes, combined with inset top boxes.

• Journal of KIISE
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• v.42 no.4
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• pp.451-458
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• 2015

Previous multimodal learning methods focus on problem-solving aspects, such as image and video search and tagging, rather than on knowledge acquisition via content modeling. In this paper, we propose the Multimodal Concept Hierarchy (MuCH), which is a content modeling method that uses a cartoon video dataset and a character-based subtitle generation method from the learned model. The MuCH model has a multimodal hypernetwork layer, in which the patterns of the words and image patches are represented, and a concept layer, in which each concept variable is represented by a probability distribution of the words and the image patches. The model can learn the characteristics of the characters as concepts from the video subtitles and scene images by using a Bayesian learning method and can also generate character-based subtitles from the learned model if text queries are provided. As an experiment, the MuCH model learned concepts from 'Pororo' cartoon videos with a total of 268 minutes in length and generated character-based subtitles. Finally, we compare the results with those of other multimodal learning models. The Experimental results indicate that given the same text query, our model generates more accurate and more character-specific subtitles than other models.

• Journal of Korea Multimedia Society
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• v.15 no.9
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• pp.1165-1173
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• 2012

One of the most popular technologies in Web2.0 is tagging, and it widely applies to Web content as well as multimedia data such as image and video. Web users have expected that tags by themselves would be reused in information search and maximize the search efficiency, but wrong tag by irresponsible Web users really has brought forth a incorrect search results. In past papers, we have gathered various information resources and tags scattered in Web, mapped one tag onto other tags, and clustered these tags according to the corelation between them. A 3-tag based search algorithm which use the clustered tags of past papers, is proposed in this paper. For performance evaluation of the proposed algorithm, our algorithm is compared with image search result of Flickr, typical tag based site, and is evaluated in accuracy and recall factor.