• Journal of Institute of Control, Robotics and Systems
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• v.11 no.12
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• pp.1083-1087
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• 2005

As development of digital technology, many kinds of multimedia data are used variously and requirements for effective use by user are increasing. In order to transfer information fast and precisely what user wants, effective retrieval method is required. As existing multimedia data are impossible to apply the MPEG-1, MPEG-2 and MPEG-4 technologies which are aimed at compression, store and transmission. So MPEG-7 is introduced as a new technology for effective management and retrieval of multimedia data. In this paper, we extract content-based features using color descriptor among the MPEG-7 standardization visual descriptor, and reduce feature data applying PCA(Principal Components Analysis) technique. We model the cerebral cortex and hippocampal neural network in engineering domain, and team content-based feature vectors fast and apply the hippocampal neural network algorithm to compose of optimized feature. And then we present fast and precise retrieval effect when indexing and retrieving.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.3
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• pp.522-538
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• 2013

Content based image retrieval has become an increasingly important research topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The retrieval systems rely on a key component, the predefined or learned similarity measures over images. We note that, the similarity measures can be potential improved if the data distribution information is exploited using a more sophisticated way. In this paper, we propose a similarity measure learning approach for image retrieval. The similarity measure, so called Fisher kernel, is derived from the probabilistic distribution of images and is the function over observed data, hidden variable and model parameters, where the hidden variables encode high level information which are powerful in discrimination and are failed to be exploited in previous methods. We further propose a discriminative learning method for the similarity measure, i.e., encouraging the learned similarity to take a large value for a pair of images with the same label and to take a small value for a pair of images with distinct labels. The learned similarity measure, fully exploiting the data distribution, is well adapted to dataset and would improve the retrieval system. We evaluate the proposed method on Corel-1000, Corel5k, Caltech101 and MIRFlickr 25,000 databases. The results show the competitive performance of the proposed method.

• The KIPS Transactions:PartD
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• v.9D no.1
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• pp.31-42
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• 2002

In this paper, we propose a new spatio-temporal representation scheme which can model moving objets trajectories effectively in video data and a new signature-based access method for moving objects trajectories which can support efficient retrieval on user query based on moving objects trajectories. The proposed spatio-temporal representation scheme supports content-based retrieval based on moving objects trajectories and concept-based retrieval based on concepts(semantics) which are acquired through the location information of moving objects trajectories. Also, compared with the sequential search, our signature-based access method can improve retrieval performance by reducing a large number of disk accesses because it access disk using only retrieved candidate signatures after it first scans all signatures and performs filtering before accessing the data file. Finally, we show the experimental results that proposed scheme is superior to the Li and Shan's scheme in terns of both retrieval effectiveness and efficiency.

• 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.

• The KIPS Transactions:PartD
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• v.12D no.5 s.101
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• pp.789-796
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• 2005

Because existing information retrieval systems, in particular library retrieval systems, use 'exact keyword matching' with user's query, they present user with massive results including irrelevant information. So, a user spends extra effort and time to get the relevant information from the results. Thus, this paper will propose SULRM a Retrieval Model using Subject Classification Table, User profile, and LSI(Latent Semantic Indexing), to provide more relevant results. SULRM uses document filtering technique for classified data and document ranking technique for non-classified data in the results of keyword-based retrieval. Filtering technique uses Subject Classification Table, and ranking technique uses user profile and LSI. And, we have performed experiments on the performance of filtering technique, user profile updating method, and document ranking technique using the results of information retrieval system of our university' digital library system. In case that many documents are retrieved proposed techniques are able to provide user with filtered data and ranked data according to user's subject and preference.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.15 no.2
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• pp.97-115
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• 2004

The paper examined the concepts and features of content-based Image and Video retrieval systems. It then analyzed the retrieval performance of on five content_based retrieval systems in terms of usability and retrieval features. The results showed that the combination of content_based retrieval techniques and meta-data based retrieval will be able to improve the retrieval effectiveness.

• Journal of information and communication convergence engineering
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• v.19 no.1
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• pp.36-41
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• 2021

Text-based image retrieval is not only cumbersome as it requires the manual input of keywords by the user, but is also limited in the semantic approach of keywords. However, content-based image retrieval enables visual processing by a computer to solve the problems of text retrieval more fundamentally. Vision applications such as extraction and mapping of image characteristics, require the processing of a large amount of data in a mobile environment, rendering efficient power consumption difficult. Hence, an effective image retrieval method on mobile platforms is proposed herein. To provide the visual meaning of keywords to be inserted into images, the efficiency of image retrieval is improved by extracting keywords of exchangeable image file format metadata from images retrieved through a content-based similar image retrieval method and then adding automatic keywords to images captured on mobile devices. Additionally, users can manually add or modify keywords to the image metadata.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.8
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• pp.1176-1189
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• 1993

This paper presents the Multiple Keyword Retrieval Method under the Object-Oriented Multimedia Database Model. The multiple keyword registration and retrieval algorithms are developed to reduce the partial matching problem in multimedia data retrieval. For this, proper storage structures of the lookup tables are designed. And also, in order to maintain the constant retrieval time, media data files are organized with B+ tree structure.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.3
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• pp.59-65
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• 2004

This paper explores an image segmentation and representation method using Vector Quantization(VQ) on color and texture for content-based image retrieval system. The basic idea is a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture space. These schemes are used for object-based image retrieval. Features for image retrieval are three color features from HSV color model and five texture features from Gray-level co-occurrence matrices. Once the feature extraction scheme is performed in the image, 8-dimensional feature vectors represent each pixel in the image. VQ algorithm is used to cluster each pixel data into groups. A representative feature table based on the dominant groups is obtained and used to retrieve similar images according to object within the image. The proposed method can retrieve similar images even in the case that the objects are translated, scaled, and rotated.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.4
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• pp.61-67
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• 2009

PACS and Medical Image System use only high level metadata in retrieving desired image nowadays. In order to retrieve Medical Image Data more efficiently, it would be needed to retrieve similarity by utilizing low level metadata as well as keyword retrieval by high level metadata. Thus, In this paper presents that it has realized similarity retrieval by low level metadata on the basis of MPEG-7, and keyword retrieval by high level metadata of DICOM base. It would be also available to look into medical image data in various methods and read accurate image promptly for diagnosis and treatment by retrieval with integrating two metadata.