• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.11
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• pp.1133-1144
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• 2019

Recently, the amount of image on the Internet has rapidly increased, due to the advancement of smart devices and various approaches to effective image retrieval have been researched under these situation. Existing image retrieval methods simply detect the objects in a image and carry out image retrieval based on the label of each object. Therefore, the semantic gap occurs between the image desired by a user and the image obtained from the retrieval result. To reduce the semantic gap in image retrievals, we connect the module for multiple objects classification based on deep learning with the module for human behavior classification. And we combine the connected modules with a behavior ontology. That is to say, we propose an image retrieval system considering the relationship between objects by using the combination of deep learning and behavior ontology. We analyzed the experiment results using walking and running data to take into account dynamic behaviors in images. The proposed method can be extended to the study of automatic annotation generation of images that can improve the accuracy of image retrieval results.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.3
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• pp.159-165
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• 2015

The need for the recognition of music emotion has become apparent in many music information retrieval applications. In addition to the large pool of techniques that have already been developed in machine learning and data mining, various emerging applications have led to a wealth of newly proposed techniques. In the music information retrieval community, many studies and applications have concentrated on tag-based music recommendation. The limitation of music emotion tags is the ambiguity caused by a single music tag covering too many subcategories. To overcome this, multiple tags can be used simultaneously to specify music clips more precisely. In this paper, we propose a novel technique to rank the proper tag combinations based on the acoustic similarity of music clips.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.8
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• pp.601-605
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• 2009

Recently, photo sharing and publishing based Social Network Sites(SNSs) are increasingly attracting the attention of academic and industry researches. Millions of users have integrated these sites into their daily practices to communicate with online people. In this paper, we propose an efficient face annotation and retrieval system under SNS. Since the system needs to deal with a huge database which consists of an increasing users and images, both effectiveness and efficiency are required, In order to deal with this problem, we propose a face annotation classifier which adopts an online learning and social decomposition approach. The proposed method is shown to have comparable accuracy and better efficiency than that of the widely used Support Vector Machine. Consequently, the proposed framework can reduce the user's tedious efforts to annotate face images and provides a fast response to millions of users.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.35-44
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• 2007

Recently, an increasing in quantity of multimedia data have brought a new problem that expected data should be retrieved fast and exactly. The adequate representation for the multimedia data is the key element for efficient retrieval. For this reason, MPEG-7 standard was established for description of multimedia data. In this paper, we propose a new approach to metadata production. The user can decompose a given content into units and easily annotate each unit by adding basic Information such as time, place, etc. as well as classification information such as event, relationship, etc. according to the MPEG-7 standard. The objective is to build automatically a pure semantic description; the nodes are the events and the links are the graphs which describe the relationships among the events. Finally, we have implemented an annotation tool(SMAT) for semantic description based on proposed technique and assess some of the experiment results. In conclusion, we ran say that the proposod annotation tool is characterized by two important proprieties : reusability and extendibility.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.11a
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• pp.355-358
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• 2003

최근 바이오인포매틱스 분야에서는 유전자 주석정보 데이터들의 통합 방안에 대한 논의가 활발하게 진행 중에 있다. 본 논문에서는 BioDAS의 웹 서비스 개념을 이용, 분산된 주석 데이터서버들간의 통합검색 시스템을 구축함으로써 메타검색 시스템을 구현하였다. 본 시스템은 사용자에게 메타검색 기능 및 결과 저장기능을 제공해 주며 외부 사용자에게 웹 서비스를 제공한다.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05d
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• pp.1069-1074
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• 2002

본 논문에서는 XML을 이용하여 멀티미디어 데이터를 구조적으로 표현하고 효율적으로 추출하기 위한 XML 기반 멀티미디어 주석 및 검색 시스템을 제안한다. 이 시스템은 멀티미디어 정보를 표현하기 위해 계층적 메타데이터 모델을 기반으로 하여 구현되였으며, 멀티미디어 데이터에 대한 주석, 검색, 브라우징 인터페이스를 제공한다. 멀티미디어에 대한 메타데이터는 MPEG-7에 정의되어 있는 서술 스키마를 기반으로 XML 스키마를 이용하여 작성하였다. 또한, 멀티미디어 데이터의 효율적인 인덱싱과 추출을 위하여 자막과 주석을 바탕으로 한 카테고라이징 기법을 사용하였다. 본 시스템의 목적은 멀티미디어 데이터에 대해 주석을 처리하고, 다양한 방법으로 검색과 그 결과를 브라우징하는 데 있다.

• 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.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.93-98
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• 2023

Instagram is one of the fastest-growing online photo social web services where users share their life images and videos with other users. Image tagging is an essential step for developing Automatic Image Annotation (AIA) methods that are based on the learning by example paradigm. Hashtags can be used on just about any social media platform, but they're most popular on Twitter and Instagram. Using hashtags is essentially a way to group together conversations or content around a certain topic, making it easy for people to find content that interests them. Practically on average, 20% of the Instagram hashtags are related to the actual visual content of the image they accompany, i.e., they are descriptive hashtags, while there are many irrelevant hashtags, i.e., stophashtags, that are used across totally different images just for gathering clicks and for search ability enhancement. Hence in this work, Sorting instagram hashtags all the way through mass tagging using HITS (Hyperlink-Induced Topic Search) algorithm is presented. The hashtags can sorted to several groups according to Jensen-Shannon divergence between any two hashtags. This approach provides an effective and consistent way for finding pairs of Instagram images and hashtags, which lead to representative and noise-free training sets for content-based image retrieval. The HITS algorithm is first used to rank the annotators in terms of their effectiveness in the crowd tagging task and then to identify the right hashtags per image.

• Journal of Information Processing Systems
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• v.7 no.3
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• pp.447-458
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• 2011

This study presents a comprehensive solution to the collection management, which is based on the model for Cultural Objects (CCO). The developed system manages and spreads the collections that are safeguarded in museums and galleries more easily by using IT. In particular, we present our approach for a non-structured search and recovery of the objects based on the annotation of artwork images. In this methodology, we have introduced a faceted search used as a framework for multi-classification and for exploring/browsing complex information bases in a guided, yet unconstrained way, through a visual interface.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2008-2019
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• 1999

As computing technologies are rapidly progressed and widely used, the needs of high quality information have been increased. To satisfy these needs, it is essential to develop a system which can provide an efficient storing, managing and retrieving mechanism of complex multimedia data, esp. video data. In this paper, we propose a metadata model which can support content-based retrieval of video data. And we design and implement an integrated user interface for querying and browser for content-based retrieval in video database which can efficiently access and browse the video clip that user want to see. Proposed query processor and browser can support various user queries by integrating image feature, spatial temporal feature and annotation. Our system supports structure browsing of retrieved result, so users can more exactly and efficiently access relevant video clip. Without browsing the whole video clip, users can know the contents of video by seeing the storyboard. This storyboard facility makes users know more quickly the content of video clip.