• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.733-736
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• 1998

In this paper, we propose a new method for content-based retrieval system using wavelet transform and correlation, which has were used in signal processing and image compressing. The matching method is used not perfect matching but similar matching. Used feature vector is the lowest frequency(LL) itself, energy value, and edge information of 4-layer, after computng a 4-layer 2-D fast wavelet transform on image. By the proosed algorithm, we got the result that was faste rand more accurate than the traditional algorithm. Because used feature vector was compressed 256:1 over original image, retrieval speed was highly improved. By using correlation, moving object with size variation was reterieved without additional feature information.

• 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 Institute of Electronics Engineers of Korea CI
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• v.46 no.4
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• pp.127-133
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• 2009

To ensure access to rapidly growing video collection, video indexing is becoming more and more essential. A database for video should be build for fast searching and extracting the accurate features of video information with more complex characteristics. Moreover, video indexing structure supports efficient retrieval of interesting contents to reflect user preferences. In this paper, we propose semantic video retrieval method based on user preference. Unlikely the previous methods do not consider user preferences. Futhermore, the conventional methods show the result as simple text matching for the user's query that does not supports the semantic search. To overcome these limitations, we develop a method for user preference analysis and present a method of video ontology construction for semantic retrieval. The simulation results show that the proposed algorithm performs better than previous methods in terms of semantic video retrieval based on user preferences.

• Proceedings of the Korea Contents Association Conference
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• 2003.05a
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• pp.416-420
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• 2003

In this paper, we implemented a dual-resolution image database system for the fast naked-eye retrieval using interpolation. This system can solve two conventional problems : a blocking noise at zoom-out image in single high resolution method and a big storage to store in simple dual-resolution image database system. The proposed method makes a subsampled image by subsampling a original image, and then a interpolated image of it using interpolation. After that, a hybrid dual-resolution image database is composed based on the differential image between the interpolated image and the original image. Experimental results of simulating through 60 sample images shows that the proposed method is 0.011 second faster than simple high-resolution method in the retrieval time - one is 0.003 second, the other is 0.014 second, respectively. Also, that improves 14.7% more than simple dual-resolution method in the stored size - one is 19,821 byte, the other is 16,910 byte, respectively.

• The Transactions of the Korea Information Processing Society
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• v.7 no.7
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• pp.2108-2115
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• 2000

The conventional Boolean retrieval systems based on vector spae model can provide the results of retrieval fast, they can't reflect exactly user's retrieval purpose including semantic information. Consequently, the results of retrieval process are very different from those users expected. This fact forces users to waste much time for finding expected documents among retrieved documents. In his paper, we designed a bayesian SOM(Self-Organizing feature Maps) in combination with bayesian statistical method and Kohonen network as a kind of unsupervised learning, then perform classifying documents depending on the semantic similarity to user query in real time. If it is difficult to observe statistical characteristics as there are less than 30 documents for clustering, the number of documents must be increased to at least 50. Also, to give high rank to the documents which is most similar to user query semantically among generalized classifications for generalized clusters, we find the similarity by means of Kohonen centroid of each document classification and adjust the secondary rank depending on the similarity.

• Journal of Korea Multimedia Society
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• v.10 no.11
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• pp.1398-1406
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• 2007

Providing natural and efficient access to the fast growing multimedia information is a critical aspect for content-based information system. Query by humming system allows the user to find a song by humming part of the tune form music database. Conventional music information retrieval systems use a high precision pitch extraction method. However, it is very difficult to extract true pitch perfectly. So, In this paper, we propose to use YIN parameter with applying the reliability to reduce the pitch extraction errors. And we describes developed music information retrieval method based on a query by humming system which uses reliable feature extraction. Developed system is based on a continuous dynamic programming algorithm with features including pitch, duration and energy along with their confidence measures. The experiment showed that the proposed method could reduce the errors of the top-10 7.2% and the top-1 9.1% compared with the cepsturm based multiple pitch candidate. The overall retrieval system achieved 92.8% correct retrieval in the top-10 rank list on a database of 155 songs.

• Journal of Korea Multimedia Society
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• v.5 no.4
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• pp.415-425
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• 2002

In this paper, we present a content-based image retrieval method which is based on the feature extraction in the wavelet transform domain. In order to overcome the drawbacks of the feature vector making up methods which use the global wavelet coefficients in subbands, we utilize the energy value of wavelet coefficients, and the shape-based retrieval of objects is processed by moment which is invariant in translation, scaling, rotation of the objects The proposed methods reduce feature vector size, and make progress performance of classification retrieval which provides fast retrievals times. To offer the abilities of region-based image retrieval, we discussed the image segmentation method which can reduce the effect of an irregular light sources. The image segmentation method uses a region-merging, and candidate regions which are merged were selected by the energy values of high frequency bands in discrete wavelet transform. The region-based image retrieval is executed by using the segmented region information, and the images are retrieved by a color, texture, shape feature vector.

• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.54-60
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• 2011

The study is content-based video retrieval system based on the news video domain as researching for the video data processing method for searching the multimedia information. For the implementation of effective system, We retrieval meaning information and special information using the knowing knowledge about formation and structure of the video data. These are possible to retrieval searching by articles fast and accurately by indexing contents the users want to search. The news domain used in experiment of our system is the KBS news on the air nowadays and precision and recall is used to evaluate the experiment and performance.

• The KIPS Transactions:PartD
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• v.18D no.1
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• pp.1-8
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• 2011

The cache structure, which is designed for assuring fast accesses to frequently accessed data, resides on the various levels of computer system hierarchies. Many studies on this cache structure have been conducted and thus many page-replacement algorithms have been proposed. Most of page-replacement algorithms are designed on the basis of heuristic methods by using their own criteria such as how recently pages are accessed and how often they are accessed. This data-retrieval process in computer systems is analogous to human memory retrieval process since the retrieval process of human memory depends on frequency and recency of the retrieval events as well. A recent study regarding human memory cognition revealed that the possibility of the retrieval success and the retrieval latency have a strong correlation with the frequency and recency of the previous retrieval events. In this paper, we propose a novel page-replacement algorithm by utilizing the knowledge from the recent research regarding human memory cognition. Through a set of experiments, we demonstrated that our new method presents better hit-ratio than the LRFU algorithm which has been known as the best performing page-replacement algorithm for DBMS caches.

• Bioinformatics and Biosystems
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• v.1 no.1
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• pp.46-50
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• 2006

According to the advancement of experimental techniques in molecular biology, genomic and protein sequence databases are increasing in size exponentially, and mean sequence lengths are also increasing. Because the sizes of these databases become larger, it is difficult to search similar sequences in biological databases with significant homologies to a query sequence. In this paper, we present the N-gram indexing method to retrieve similar sequences fast, precisely and comparably. This method regards a protein sequence as a text written in language of 20 amino acid codes, adapts N-gram tokens of fixed-length as its indexing scheme for sequence strings. After such tokens are indexed for all the sequences in the database, sequences can be searched with information retrieval algorithms. Using this new method, we have developed a protein sequence search system named as ProSeS (PROtein Sequence Search). ProSeS is a protein sequence analysis system which provides overall analysis results such as similar sequences with significant homologies, predicted subcellular locations of the query sequence, and major keywords extracted from annotations of similar sequences. We show experimentally that the N-gram indexing approach saves the retrieval time significantly, and that it is as accurate as current popular search tool BLAST.