• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4355-4371
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• 2020

Phase retrieval, recovering a signal from phaseless measurements, is generally considered to be an NP-hard problem. This paper adopts an amplitude-based nonconvex optimization cost function to develop a new stochastic gradient algorithm, named stochastic reweighted phase retrieval (SRPR). SRPR is a stochastic gradient iteration algorithm, which runs in two stages: First, we use a truncated sample stochastic variance reduction algorithm to initialize the objective function. The second stage is the gradient refinement stage, which uses continuous updating of the amplitude-based stochastic weighted gradient algorithm to improve the initial estimate. Because of the stochastic method, each iteration of the two stages of SRPR involves only one equation. Therefore, SRPR is simple, scalable, and fast. Compared with the state-of-the-art phase retrieval algorithm, simulation results show that SRPR has a faster convergence speed and fewer magnitude-only measurements required to reconstruct the signal, under the real- or complex- cases.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.2
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• pp.12-17
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• 2003

This paper proposes content-based image retrieval system with fuzzy ART neural network algorithm. Retrieving large database of image data, the clustering is essential for fast retrieval. However, it is difficult to cluster huge image data pertinently, Because current retrieval methods using similarities have several problems like low accuracy of retrieving and long retrieval time, a solution is necessary to complement these problems. This paper presents a content-based image retrieval system with neural network in order to reinforce abovementioned problems. The retrieval system using fuzzy ART algorithm normalizes color and texture as feature values of input data between 0 and 1, and then it runs after clustering the input data. The implemental result with 300 image data shows retrieval accuracy of approximately 87%.

• The KIPS Transactions:PartA
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• v.10A no.6
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• pp.737-748
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• 2003

With the popularity of digital music contents, querying and retrieving music contents efficiently from database has become essential. In this paper, we propose a Fast Melody Finder (FMF) that can retrieve melodies fast and efficiently from music database using frequently queried tunes. This scheme is based on the observation that users have a tendency to memorize and query a small number of melody segments, and indexing such segments enables fast retrieval. To handle those tunes, FMF transcribes all the acoustic and common music notational inputs into a specific string such as UDR and LSR. We have implemented a prototype system and showed on its performance through various experiments.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2S
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• pp.678-691
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• 2000

A variety of multimedia applications require multimedia database management systems to manage multimedia data, such as text, image, and video, as well as t support content-based image or video retrieval. In this paper we design and implement a content-based image retrieval system, BADA-IV/I$^2$R(Image Information Retrieval), which is developed based on BADA-IV multimedia database management system. In this system image databases can be efficiently constructed and retrieved with the visual features, such as color, shape, and texture, of image. we extend SQL statements to define image query based on both annotations and visual features of image together. A high-dimensional index structure, called CIR-tree, is also employed in the system to provide an efficient access method to image databases. We show that BADA-IV/I$^2$R provides a flexible way to define query for image retrieval and retrieves image data fast and effectively: the effectiveness and performance of image retrieval are shown by BEP(Bull's Eye Performance) that is used to measure the retrieval effectiveness in MPEG-7 and comparing the performance of CIR-tree with those of X-tree and TV-tree, respectively.

• Journal of the Korea Society of Computer and Information
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• v.15 no.12
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• pp.101-108
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• 2010

In this paper, how fast and accurate the companies provides exponentially increasing information to the users is the most important in the corporate competitiveness. The enhancement of the retrieval relevance became the important element in enhancing company competitiveness and it is required to provide the services that are beyond simple retrieval service for good quality search service. This paper proposes the effective scheme that enhances retrieval relevance by utilizing registered tag information. By proposed scheme, we can overcome the limitations of retrieval relevance that usual search engines provide. And we compare the proposed scheme with existing web retrieval service on retrieval relevance evaluation and related search keyword.

• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.73-84
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• 2005

An increasing in quantity of multimedia data brought a new problem that expected data should be retrieved fast and exactly. The adequate representation is a key element for the efficient retrieval For this reason, the MPEG-7 standard was established for description of multimedia contents in 2001. Recently, the new metadata were developed to represent characteristics of movie information resources by Korea Federation of Film Archives. In this paper. we designed and implemented a movie information retrieval system. This system used XML schema to accept movie information metada. This system offers a keyword retrieval using high-level metadata based on movie information and similarity retrieval using low-level metadata based on MPEG-7. As a result, that ill be Possible more efficient movie information interchange . movie information metadata reuse and fast retrieve.

• Journal of The Korean Association of Information Education
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• v.3 no.2
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• pp.72-84
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• 2000

Information retrieval system plays a key role in the information society to store digital documents with efficiency and to provide user with the information through the retrieval very fast. Especially, indexing is a prerequisite function for the information retrieval system in order to retrieve the information of the documents effectively which are saved in database. In this paper, we propose an indexing method using text partition. This method can retrieve educational documents in short processing time. We applied the suggested indexing method to real information retrieval system, and proved its excellent functions through the demonstration.

• Journal of Broadcast Engineering
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• v.1 no.2
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• pp.96-107
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• 1996

In case of remote image retrieval via shared network or low speed link in order to make a decision for target image problems such as transmission delay are encountered. In this paper browsing and optimization techniques are proposed for fast retrieval of Image by the multiresolution representation and progressive transmission. The proposed network model was analyzed and evaluated for system's performance improvement. Interactive user-system using several multiresolution representation has shown better performance in transmission delay minimization over the single resolution image retrieval system.

• Convergence Security Journal
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• v.8 no.4
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• pp.91-96
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• 2008

An increasing in quantity of multimedia data brought a new problem that expected data should be retrieved fast and exactly. The adequate representation is a key element for the efficient retrieval. For this reason, MPEG-7 standard was established for description of multimedia data in 2001. In this paper, we designed a Audio/Image Retrieval System based on MPEG-7 that can retrieve multimedia data like audio, image efficiently. And we integrated high-level and low-level schemas to retrieve datas for users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1403-1417
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• 2019

With the rapid growth of multimedia data, research on cross-media feature learning has significance in many applications, such as multimedia search and recommendation. Existing methods are sensitive to noise and edge information in multimedia data. In this paper, we propose a semi-supervised method for cross-media feature learning by means of $L_{2,q}$ norm to improve the performance of cross-media retrieval, which is more robust and efficient than the previous ones. In our method, noise and edge information have less effect on the results of cross-media retrieval and the dynamic patch information of multimedia data is employed to increase the accuracy of cross-media retrieval. Our method can reduce the interference of noise and edge information and achieve fast convergence. Extensive experiments on the XMedia dataset illustrate that our method has better performance than the state-of-the-art methods.