• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.364-366
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• 2005

From the 90's, the image information retrieval methods have been on progress. As good examples of the methods, Conventional histogram method and merged-color histogram method were introduced. They could get good result in image retrieval. However, Conventional histogram method has disadvantages if the histogram is shifted as a result of intensity change. Merged-color histogram, also, causes more process so, it needs more time to retrieve images. In this paper, we propose an improved new method using Adaptive Color Histogram Bin Matching(AHB) in image retrieval. The proposed method has been tested and verified through a number of simulations using hundreds of images in a database. The simulation results have Quickly yielded the highly accurate candidate images in comparison to other retrieval methods. We show that AHB's can give superior results to color histograms for image retrieval.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.949-954
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• 2005

From the 90's, the image information retrieval methods have been on progress. As good examples of the methods, Conventional histogram method and merged-color histogram method were introduced. Dey could get good result in image retrieval. However, Conventional histogram method has disadvantages if the histogram is shifted as a result of intensity change. Merged-color histogram, also, causes more process so, it needs more time to retrieve images. In this paper, we propose an improved new method using Adaptive Color Histogram(ACH) in image retrieval. The proposed method has been tested and verified through a number of simulations using hundreds of images in a database. The simulation results have quickly yielded the highly accurate candidate images in comparison to other retrieval methods. We show that ACH's can give superior results to color histograms for image retrieval.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.4
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• pp.324-328
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• 2012

With the recent advances in information retrieval, cross-media retrieval has been attracting lot of attention, but several issues remain problems such as constructing effective correlations, calculating similarity between different kinds of media objects. To gain better cross-media retrieval performance, it is crucial to mine the semantic correlations among the heterogeneous multimedia data. This paper introduces a new method for cross-media retrieval which uses ontology to organize different media objects. The experiment results show that the proposed method is effective in cross-media retrieval.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.1
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• pp.143-149
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• 2010

In ubiquitous environment, information retrieval using collaborative filtering is a popular technique for reducing information overload. Collaborative filtering systems can produce personal recommendations by computing the similarity between your preference and the one of other people. We integrate the collaboration filtering method and context-aware information retrieval method. The proposed method enables to find some relevant information to specific user's contexts. It aims to makes more effective information retrieval to the users. The proposed method is conceptually comprised of two main tasks. The first task is to tag context tags by automatic tagging technique. The second task is to recommend items for each user's contexts integrating collaborative filtering and information retrieval. We describe a new integration method algorithm and then present a u-commerce application prototype.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.593-596
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• 1999

In this paper, we propose an image retrieval system using the MBCM(Modified Borda Count method) in CME(Combining Multiple Experts). It combines color-, shape- and texture-based retrieval sub-systems. CME method can complementarily combine results of each retrieval system, which uses different features. There are some problems when the Borda count method in pattern recognition is applied to image retrieval. Thus, we propose a modified Borda count method to solve these problems. In the experiment, our method reduces false positive errors and produces better results than that of each retrieval module that uses only one feature.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.1
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• pp.21-31
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• 2020

Internet of Things is growing rapidly. As it evolves, the amount of data is increasing significantly. It requires a new personalized information retrieval method. Internet of Things is defined as uniquely identifiable interoperable connected object. The first definition of Internet of Things was from Things oriented perspective. However, previous studies about personalized information retrieval method do not consider Things. To meet user's individual needs, previous studies concentrate on only human, not Things. In this paper, we propose a personalized information retrieval method considering participating device in Internet of Things. It provides personalized information using data type preference for each device. Moreover, it provides personalized results by integrating data type preference for set of devices. This paper describes a new personalized retrieval method and algorithm. It consists of five steps. Then, it presents four scenarios using proposed method. The scenarios show our work is more effective and efficient than existing one.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.3
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• pp.17-26
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• 2021

Internet of Things is an infrastructure of the interconnected devices. In Internet of Things environment, many smart devices are used in daily life. It requires a new retrieval method using multiple devices. We propose an information retrieval method using both device characteristics and device usage characteristics in multi-device environments. Firstly, information retrieval is performed using a general purpose device. And then, it is performed using dedicated devices. Our method uses both characteristics of the devices and usage characteristics of them. Moreover, it considers queries on the general purpose device. This paper proposes a new retrieval method and describes algorithms. Then, it presents smart home scenarios. Performance evaluation is performed using the scenarios. The evaluation results show higher precision and efficiency than previous researches. The proposed method gets information more accurately and quickly in IOT multiple device environments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.5
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• pp.889-898
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• 1997

Most general content-based image retrieval techniques use color and texture as retrieval indices. Spatial information is not used to color histogram and color pair based on color retrieval techniques. This paper proposes the selection of a set of representative in the duplicated color histogram, the analysis of spatial information of the selected colors and the image retrieval process based on the duplicated color histogram and spatial information. Two color historgrams for background and object are used in order to decide on color selection in the duplicated color histogram. Spatial information is obtained using a maximum entropy discretization. A retrieval process applies to duplicated color histogram and spatial to retrieve input images and relevant images. As the result of experiment of the image retrieval, improved color his togram and spatial information method hs increased the retrieval effectiveness more the color histogram method and color pair method.

• Journal of Information Processing Systems
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• v.9 no.3
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• pp.349-364
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• 2013

The big challenge in current content-based image retrieval systems is to reduce the semantic gap between the low level-features and high-level concepts. In this paper, we have proposed a novel framework for efficient image retrieval to improve the retrieval results significantly as a means to addressing this problem. In our proposed method, we first extracted a strong set of image features by using the dual-tree rotated complex wavelet filters (DT-RCWF) and dual tree-complex wavelet transform (DT-CWT) jointly, which obtains features in 12 different directions. Second, we presented a relevance feedback (RF) framework for efficient image retrieval by employing a support vector machine (SVM), which learns the semantic relationship among images using the knowledge, based on the user interaction. Extensive experiments show that there is a significant improvement in retrieval performance with the proposed method using SVMRF compared with the retrieval performance without RF. The proposed method improves retrieval performance from 78.5% to 92.29% on the texture database in terms of retrieval accuracy and from 57.20% to 94.2% on the Corel image database, in terms of precision in a much lower number of iterations.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1515-1518
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• 2003

This paper suggests the condensed two-stage retrieval method for fast image retrieval in the content-based image retrieval system, and proves the validity of the performance. The condensed two-stage retrieval method reduces the overall response time remarkably while it maintains relevance with the conventional exhaustive search method. It is explained by properties of the Cauchy-Schwartz inequality. In experimental result, it turns out that there is an optimal value of condensation ratio which minimizes the overall response time. We analyze the optimal condensation ratio by modeling a similarity computation time mathematically.