• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.77-80
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• 2001

In general, the informations of the inner image that user interested in are limited to a special domain. In this paper, as using Wavelet Transform for dividing image into high frequency and low frequency, We can separate foreground including many data. After calculating object boundary of separated part, We extract special features using Color Coherence Vector. According to results of this experiment, the method of comparing data extracting foreground features is more effective than comparing data extracting features of entire image when we extract the image user interested in.

• The KIPS Transactions:PartB
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• v.13B no.1 s.104
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• pp.47-52
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• 2006

This paper proposes a method of content-based image retrieval using region feature vector in order to overcome disadvantages of existing color histogram methods. The color histogram methods have a weak point that reduces accuracy because of quantization error, and more. In order to solve this, we convert color information to HSV space and quantize hue factor being purecolor information and calculate histogram and then use thus for retrieval feature that is robust in brightness, movement, and rotation. Also we solve an insufficient part that is the most serious problem in color histogram methods by dividing an image into sixteen regions and then comparing each region. We improve accuracy by edge and DC of DCT transformation. As a result of experimenting with 1,000 color images, the proposed method has showed better precision than the existing methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.73-80
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• 2005

Recently there have been many efforts to support searching and browsing based on the visual content of image and multimedia data. Most existing approaches to content-based image retrieval rely on query by example or user based low-level features such as color, shape, texture. But these methods of query are not easy to use and restrict. In this paper we propose a method for automatic color object extraction and labelling to support multiple queries of content-based image retrieval system. These approaches simplify the regions within images using single colorizing algorithm and extract color object using proposed Color and Spatial based Binary tree map(CSB tree map). And by searching over a large of number of processed regions, a index for the database is created by using proposed labelling method. This allows very fast indexing of the image by color contents of the images and spatial attributes. Futhermore, information about the labelled regions, such as the color set, size, and location, enables variable multiple queries that combine both color content and spatial relationships of regions. We proved our proposed system to be high performance through experiment comparable with another algorithm using 'Washington' image database.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.3
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• pp.97-102
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• 2008

In this paper we propose an object-based image retrieval method using spatial color model and feature points registration method for an effective color query detection. The proposed method in other to overcome disadvantages of existing color histogram methods and then this method is use the HMMD model and rough set in order to segment and detect the wanted image parts as a real time without the user's manufacturing in the database image and query image. Here, we select candidate regions in the similarity between the query image and database image. And we use SIFT registration methods in the selected region for object retrieving. The experimental results show that the proposed method is more satisfactory detection radio than conventional method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8B
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• pp.1413-1423
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• 2000

Color distribution is changed according to the variation of illumination position and illumination color. Therefore, even if images are relevant each other, retrieval accuracy is degraded. In this paper, we propose the image retrieval method using color information excluded illumination component. The proposed dynamic range control method removes the shadow region generated by change of illumination position to increase the color discrimination power. To exclude the illuminant color, we use the diffuse reflection component of object and gray world assumption. The experimental results show that the color histogram method using color information excluded illuminant has higher retrieval accuracy than conventional color histogram using the color information of input image.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3A
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• pp.258-267
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• 2002

We present an image retrieval method for improving retrieval performance by effective fusion of entropy features in wavelet region and wavelet moments. In this method, entropy features are sensitive to the local variation of gray level and well extract valley and edges. These features are effectively applied to contend-based image retrieval by well fusing to wavelet moments that represent texture property in multi-resolution. In order to evaluate the performance of the proposed method. We use Corel Draw Photo DB. Experiment results show that the proposed yields 11% better performance for Corel Draw Photo DB over wavelet moments method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.8
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• pp.1358-1364
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• 2002

Presently, Multimedia data are increasing suddenly in broadcasting and internet fields. For retrieval of still images in multimedia database, content-based image retrieval system is implemented in this paper that user can retrieve similar objects from image database after choosing a wanted query region of object. As to extract color features from query image, we transform color to HSV with proposed method that similarity is obtained it through histogram intersection with database images after making histogram. Also, query image is transformed to gray image and induced to wavelet transformation by which spatial gray distribution and texture features are extracted using banded autocorrelogram and GLCM before having similarity values. And final similarity values is determined by adding two similarity values. In that, weight value is applied to each similarity value. We make up for defects by taking color image features but also gray image features from query image. Elevations of recall and precision are verified in experiment results.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.1
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• pp.116-125
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• 2002

This paper presented a method of extracting shape information from a clipart image and then measured the similarity between clipart images using the extracted shape information. The results indicated that the outlines of the extracted clipart images were clearer that those of the original images. Previous methods of extracting shape information could be classified into outline-based methods and region-based methods. Included in the former category, the proposed method expressed the convex and concave aspects of an outline using the ratio of a rectangle. Accordingly, the proposed method was superior in expressing shape information than previous outline-based feature methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9B
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• pp.1713-1720
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• 1999

This paper presents a new key frame extraction technique, for scene change detection, using the proposed AHIM (Accumulative Histogram Intersection Measure) from the DC image constructed by DCT DC coefficients in the compressed video sequence that is video compression standard such as MPEG. For fast content-based browsing and video retrieval in a video database, we also provide a novel coarse-to-fine video indexing scheme. In the extracted key frame, we perform the region segmentation as a preprocessing. First, the segmented image is projected with the horizontal direction, then we transform the result into a histogram, which is saved as a database index. In the second step, we calculate the moments and change them into a distance value. From the simulation results, the proposed method clearly shows the validity and superiority in respect of computation time and memory space, and that in conjunction with other techniques for indexing, such as color, can provide a powerful framework for image indexing and retrieval.

• The KIPS Transactions:PartB
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• v.14B no.5
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• pp.329-336
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• 2007

In this paper we propose both the construction of image DB including information on the shooting location and direction of the captured outdoor images and the efficient retrieval method from the DB. Furthermore, for the automatic extraction of the location and direction information, we suggest to have the Digital Camera equipped with an expandable GPS modulo which has a function to calculate the location and direction and also to utilize GPS IFD tags in the EXIF. Then that will make it possible for us to retrieve quickly and precisely the target image with its geography and other objects on the ground included. In the previous retrieval method based only on the location, we eel some extra useless images due to the fact that all the images in the ROI(Region Of Interest) are searched on one condition, radius. However, with the proposed method in this paper, we can not only retrieve all the images selectively within the ROI but also achieve nearly 100% of precision when we search for the target images within DOI(Direction Of Interest) with another condition, direction, added. Applying this method to an image retrieval system, we can classify or retrieve natural images based on the location and direction information, which, in turn, will be vitally useful to diverse industrial fields such as disaster alarm system, fire and disaster prevention system, traffic information system, and so forth.