• Journal of Korea Society of Industrial Information Systems
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• v.7 no.4
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• pp.9-16
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• 2002

Content based Image retrieval methods in the multimedia information retrievals use primary visual features such as color, texture and shape. Color and texture generally are used as features of the image retrieval systems. But these systems may produce errors in similar image retrieval because two images with different shapes can represent very different contents. Therefore, the use of shape describing features is essential in an efficient content based image retrieval system. In this paper, after the global features filtering process by the boundary of objects, we have created a better shape similarity image retrieval system by a histogram of shape information.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.711-712
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• 2008

We developed a XMAS (X Multimedia Analysis System) for analyzing the illegal and objectionable multimedia in Internet environment based on Web2.0. XMAS uses the MPEG-7 visual descriptor and multi-class SVM (support vector machine) and its performance (accuracy on precision) is about 91.6% for objectionable multimedia analysis and 99.9% for illegal movie retrieval.

• Journal of the Korean Society for Library and Information Science
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• v.32 no.3
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• pp.251-264
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• 1998

The purpose of this study is to build a Web Based IT Trend Retrieval System using Visual Foxpro. In this system, we can retrieve recent titles of Information Technology Trend articles. Besides, all of the titles are linked to the contents of the articles. For this purpose, FoxWeb and Personal Web Server was utilized also.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.769-786
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• 2016

Object recognition and object location have always drawn much interest. Also, recently various computational models have been designed. One of the big issues in this domain is the lack of an appropriate model for extracting important part of the picture and estimating the object place in the same environments that caused low accuracy. To solve this problem, a new Salient Based Bag of Visual Word (SBBoVW) model for object recognition and object location estimation is presented. Contributions lied in the present study are two-fold. One is to introduce a new approach, which is a Salient Based Bag of Visual Word model (SBBoVW) to recognize difficult objects that have had low accuracy in previous methods. This method integrates SIFT features of the original and salient parts of pictures and fuses them together to generate better codebooks using bag of visual word method. The second contribution is to introduce a new algorithm for finding object place based on the salient map automatically. The performance evaluation on several data sets proves that the new approach outperforms other state-of-the-arts.

• Journal of the Semiconductor & Display Technology
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• v.7 no.2
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• pp.69-76
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• 2008

Recently, as the multimedia processing application increases rapidly by going on increasing multimedia data, the efficient retrieval method of image information is required in many fields of application and becoming the matter of major concern. Furthermore, in the last few years rapid improvements in hardware technology have made it possible to process, store and retrieve huge amounts of data in a multimedia format. As a result, Content-Based Image Retrieval (CBIR) has been receiving widespread interest during the last decade. This paper propose the content-based retrieval system as a method for performing image retrieval through the effective feature analysis of the object of significant meaning by using YCbCr channel merging on the basis of the characteristics of man's visual system.

• Journal of Korea Multimedia Society
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• v.7 no.9
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• pp.1312-1320
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• 2004

Recently, even if a lot of visual feature representations have been studied and systems have been built, there is a limit to existing content-based image retrieval mechanism in its availability. One of the limits is the gap between a user's high-level concepts and a system's low-level features. And human beings' subjectivity in perceiving similarity is excluded. Therefore, correct visual information delivery and a method that can retrieve the data efficiently are required. Relevance feedback can increase the efficiency of image retrieval because it responds of a user's information needs in multimedia retrieval. This paper proposes an efficient CBIR introducing positive and negative relevance feedback with threshold value and pre-fetching to improve the performance of conventional relevance feedback mechanisms. With this Proposed feedback strategy, we implement an image retrieval system that improves the conventional retrieval system.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2008.10a
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• pp.5-8
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• 2008

This study proposed the Knowledge Base Building method reflecting the user's preferences based on the fuzzy set theory to develop information contents which support pedestrian's navigation. This research evaluated subject's preferences on the commercial spaces set to the hypothetical destination. Also it surveyed the causal relationship between the visual characteristics and the emotional characteristics to propose the methods of Navigation Knowledge Base (NKB). The NKB was composed by three elements; 1.the correlation model between emotional characteristics, 2.the causal relationship between visual characteristics and emotional characteristics, 3.the transformation model between visual characteristics and the physical characteristics.

• The Journal of the Korea Contents Association
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• v.4 no.1
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• pp.18-23
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• 2004

In this paper, we propose a new visual feature extraction method for content-based image retrieval(CBIR) based on wavelet transform which has both spatial-frequency characteristic and multi-resolution characteristic. We extract visual features for each frequency band in wavelet transformation and use them to CBIR. The lowest frequency band involves spacial information of original image. We extract L feature vectors using fuzzy homogeneity in the wavelet domain, which consider both the wavelet coefficients and the spacial information of each coefficient. Also, we extract 3 feature vectors wing the energy values of high frequency bands, and store those to image database. As a query, we retrieve the most similar image from image database according to the 10 largest homograms(normalized fuzzy homogeneity vectors) and 3 energy values. Simulation results show that the proposed method has good accuracy in image retrieval using 90 texture images.

• ETRI Journal
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• v.42 no.3
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• pp.399-410
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• 2020

Detecting visual relationships in an image is important in an image understanding task. It enables higher image understanding tasks, that is, predicting the next scene and understanding what occurs in an image. A visual relationship comprises of a subject, a predicate, and an object, and is related to visual, language, and spatial cues. The predicate explains the relationship between the subject and object and can be categorized into different categories such as prepositions and verbs. A large visual gap exists although the visual relationship is included in the same predicate. This study improves upon a previous study (that uses language cues using two losses) and a spatial cue (that only includes individual information) by adding relative information on the subject and object of the extant study. The architectural limitation is demonstrated and is overcome to detect all zero-shot visual relationships. A new problem is discovered, and an explanation of how it decreases performance is provided. The experiment is conducted on the VRD and VG datasets and a significant improvement over previous results is obtained.

• The Transactions of the Korea Information Processing Society
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• v.4 no.9
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• pp.2315-2332
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• 1997

In this paper, we implement a content based image retrieval system for image searching by visual features from the image databases on WWW (world wide web). The image retrieval system finds the images that contain the most similar color regions after the system automatically extracts color features from the input image. We can select one of two query methods which use a full image of $4{\times}4$ 16 sketched color region. The image similarity is calculated on the histogram intersection distance and the histogram Euclidean distance. As the experimental results show that the two different query types provide the precision/recall 0.84/0.92 and 0.85/0.93 respectively, this retrieval system has been able to obtain high performance and validity.