• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.2
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• pp.1-8
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• 2003

This paper proposes an object-based image retrieval technique based on the dominant color pair information. Most of existing methods for content based retrieval extract the features from an image as a whole, instead of an object of interest. As a result, the retrieval performance tends to degrade due to the background colors. This paper proposes an object based retrieval scheme, in which an object of interest is used as a query and the similarity is measured on candidate regions of DB images where the object may exist. From the segmented image, the dominant color pair information between adjacent regions is used for selecting candidate regions. The similarity between the query image and DB image is measured by using the color correlogram technique. The dominant color pair information is robust against translation, rotation, and scaling. Experimental results show that the performance of the proposed method has been improved by reducing the errors caused by background colors.

• The Transactions of the Korea Information Processing Society
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• v.6 no.3
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• pp.603-614
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• 1999

As multimedia application services on high-speed information network have been rapidly developed, the need for the video information management system that provides an efficient way for users to retrieve video data is growing. In this paper, we propose a video data model that integrates free annotations, image features, and spatial-temporal features for video purpose of improving content-based retrieval of video data. The proposed video data model can act as a generic video data model for multimedia applications, and support free annotations, image features, spatial-temporal features, and structure information of video data within the same framework. We also propose the video query language for efficiently providing query specification to access video clips in the video data. It can formalize various kinds of queries based on the video contents. Finally we design and implement the query processing engine for efficient video data retrieval on the proposed metadata model and the proposed video query language.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.665-672
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• 2012

Amid rapidly increasing imagery inputs and their volume in a remote sensing imagery database, Content-Based Image Retrieval (CBIR) is an effective tool to search for an image feature or image content of interest a user wants to retrieve. It seeks to capture salient features from a 'query' image, and then to locate other instances of image region having similar features elsewhere in the image database. For a CBIR approach that uses texture as a primary feature primitive, designing a texture descriptor to better represent image contents is a key to improve CBIR results. For this purpose, an extended feature vector combining the Gabor filter and co-occurrence histogram method is suggested and evaluated for quantitywise and qualitywise retrieval performance criterion. For the better CBIR performance, assessing similarity between high dimensional feature vectors is also a challenging issue. Therefore a number of distance metrics (i.e. L1 and L2 norm) is tried to measure closeness between two feature vectors, and its impact on retrieval result is analyzed. In this paper, experimental results are presented with several CBIR samples. The current results show that 1) the overall retrieval quantity and quality is improved by combining two types of feature vectors, 2) some feature is better retrieved by a specific feature vector, and 3) retrieval result quality (i.e. ranking of retrieved image tiles) is sensitive to an adopted similarity metric when the extended feature vector is employed.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.20-27
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• 2003

In this paper, we implemented a content-based image retrieval system that user can choose a wanted query region of object and retrieve similar object from image database. Query image is induced to wavelet transformation after divided into hue components and gray components that hue features is extracted through color autocorrelogram and dispersion in hue components. Texture feature is extracted through autocorrelogram and GLCM in gray components also. Using features of two components, retrieval is processed to compare each similarity with database image. In here, weight value is applied to each similarity value. We make up for each defect by deriving features from two components beside one that elevations of recall and precision are verified in experiment results. Moreover, retrieval efficiency is improved by weight value. And various features of database images are indexed automatically in feature library that make possible to rapid image retrieval.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.126-136
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• 2004

This paper proposes a new content-based image retrieval algorithm using color-spatial information. For the purpose, the paper suggests two kinds of indexing key to prune away irrelevant images to a given query image; MCS(Main Colors' Set), which is related with color information and DBS (Distribution Block Signature), which is related with spatial information. After successively applying these filters to a database, we could get a small amount of high potential candidates that are somewhat similar to the query image. Then we would make use of new QM(Quad modeling) and relevance feedback mechanism to obtain more accurate retrieval. It would enhance the retrieval effectiveness by dynamically modulating the weights of color-spatial information. Experiments show that the proposed algorithm can apply successfully image retrieval applications.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.9 no.2
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• pp.73-79
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• 2010

The most popular technique for image retrieval in a heterogeneous collection of color images is the comparison of images based on their color histogram. The color histogram describes the distribution of colors in the color space of a color image. In the most image retrieval systems, the color histogram is used to compute similarities between the query image and all the images in a database. But, small changes in the resolution, scaling, and illumination may cause important modifications of the color histogram, and so two color images may be considered to be very different from each other even though they have completely related semantics. A new method of color feature representation based on the 3-dimensional RGB color map is proposed to improve the defects of the color histogram. The proposed method is based on the three 2-dimensional projection map evaluated by projecting the RGB color space on the RG, GB, and BR surfaces. The experimental results reveal that the proposed is less sensitive to small changes in the scene and that achieve higher retrieval performances than the traditional color histogram.

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.309-314
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• 2006

Recently, multimedia DBMS is appeared to be the core technology of the information society to store, manage and retrieve multimedia data efficiently. In this paper, we propose a new method for content based-retrieval system using wavelet transform, energy value to extract automatically feature vector from image data, and suggest an effective retrieval technique through this method. Wavelet transform is widely used in image compression and digital signal analysis, and its coefficient values reflect image feature very well. The correlation in wavelet domain between query image data and the stored data in database is used to calculate similarity. In order to assess the image retrieval performance, a set of hundreds images are run. The method using standard derivation and mean value used for feature vector extraction are compared with that of our method based on energy value. For the simulation results, our energy value method was more effective than the one using standard derivation and mean value.

• The Transactions of the Korea Information Processing Society
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• v.4 no.6
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• pp.1418-1432
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• 1997

Most general content_based image retrieval techniques use color and texture as retrieval indices. In color techniques, color histogram and color pair based color retrieval techniques suffer from a lack of spatial information and text. And This paper describes the design and implementation of content_based image retrieval system using color space and keywords. The preprocessor for image retrieval has used the coordinate system of the existing HSI(Hue, Saturation, Intensity) and preformed to split One image into chromatic region and achromatic region respectively, It is necessary to normalize the size of image for 200*N or N*200 and to convert true colors into 256 color. Two color histograms for background and object are used in order to decide on color selection in the color space. Spatial information is obtained using a maximum entropy discretization. It is possible to choose the class, color, shape, location and size of image by using keyword. An input color is limited by 15 kinds keyword of chromatic and achromatic colors of the Korea Industrial Standards. Image retrieval method is used as the key of retrieval properties in the similarity. The weight values of color space ${\alpha}(%)and\;keyword\;{\beta}(%)$ can be chosen by the user in inputting the query words, controlling the values according to the properties of image_contents. The result of retrieval in the test using extracted feature such as color space and keyword to the query image are lower that those of weight value. In the case of weight value, the average of te measuring parameters shows approximate Precision(0.858), Recall(0.936), RT(1), MT(0). The above results have proved higher retrieval effects than the content_based image retrieval by using color space of keywords.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.720-732
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• 1996

Spatial match retrieval methods for iconic image databases recognize an image document as several icon symbols. Therefore the iconic symbols are used as primary keys to index the image document. When a user requires content-based retrieval ofimages, a spatial match retrieval method converts a query image into iconic symbols and then retrieves relevant images by accessing stored images. In order to support content-based image retrieval efficiently, we, in this paper, propose spatial match retrieval methods using signatures for iconic image databases. For this, we design new index representations of two-dimensional iconic images and explain implemented system.. In addition, we compare the conventional 9-DLT and our two-dimensional image retrieval method in terms of retrieval precision and recall ratio. We show that our method is more efficient than the conventional method.

• Journal of the Semiconductor & Display Technology
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• v.7 no.4
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• pp.57-62
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• 2008

Image retrieval is one of the most exciting and rapidly growing research issues in the field of multimedia technology. This paper proposes a new method that performs search the relevant images by using query-by-example. The proposed method for search and retrieval of images utilizes the location information where the image had been taken. The system associates the photo images with their corresponding GPS coordinates that are used as metadata for searching. Experimental results show that the proposed method demonstrates better performance improving up to 59% of average recall and 49% of average precision. Moreover, we learned from the experimental results geo-location information embedded within the image header is more effective and positive on the search and storage.