• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.1-9
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• 2007

Semantic photo retrieval has been an important role in reducing the semantic gap between the simple visual features and the abundant semantics delivered by a photo. Effective photo retrieval using semantics is one of the major challenges in photo retrieval. And we propose a new event semantic photo retrieval method by using photo annotation user interface. In this paper, A photo album management system that facilitates photo management and semantic retrieval, which fully relies on the MPEG-7 standard as an information base and a native XML database, has been designed and implemented.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1491-1499
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• 2011

Now a days, to make good use of tags is a general tendency when users need to upload or search some multimedia data such as images and videos on the Web. In this paper, we introduce an approach to calculate semantic importance of tags and to make re-ranking with them on tagged Web image retrieval. Generally, most photo images stored on the Web have lots of tags added with user's subjective judgements not by the importance of them. So they become the cause of precision rate decrease with simple matching of tags to a given query. Therefore, if we can select semantically important tags and employ them on the image search, the retrieval result would be enhanced. In this paper, we propose a method to make image retrieval re-ranking with the key tags which share more semantic information with a query or other tags based on Wikipedia-based semantic relatedness. With the semantic relatedness calculated by using huge on-line encyclopedia, Wikipedia, we found the superiority of our method in precision and recall rate as experimental results.

• KIISE Transactions on Computing Practices
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• v.22 no.6
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• pp.290-295
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• 2016

Recently, folksonomy-based image-sharing sites where users cooperatively make and utilize tags of image annotation have been gaining popularity. Typically, these sites retrieve images for a user request using simple text-based matching and display retrieved images in the form of photo stream. However, these tags are personal and subjective and images are not categorized, which results in poor retrieval accuracy and low user satisfaction. In this paper, we propose a categorization scheme for folksonomy images which can improve the retrieval accuracy in the tag-based image retrieval systems. Consequently, images are classified by the semantic similarity using text-information and image-information generated on the folksonomy. To evaluate the performance of our proposed scheme, we collect folksonomy images and categorize them using text features and image features. And then, we compare its retrieval accuracy with that of existing systems.