• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.2
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• pp.71-79
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• 2008

This study proposed a content-based image retrieval system that uses RGB maximum frequency indexing and BW clustering in order to deal with existing retrieval errors using histogram. We split RGB from RGB color images, obtained histogram which was evenly split into 32 bins, calculated and analysed pixels of each area at histogram of R, G, B and obtained the maximum value. We indexed the color information obtained, obtained 100 similar images using the values, operated the final image retrieval system using the total number and distribution rate of clusters. The algorithm proposed in this study used space information using the features obtained from R, G, and B and clusters to obtain effective features, which overcame the disadvantage of existing gray-scale algorithm that perceived different images as same if they have the same frequencies of shade. As a result of measuring the performances using Recall and Precision, this study found that the retrieval rate and priority of the proposed algorithm are more outstanding than those of existing algorithm.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.223-230
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• 2003

Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (e 'g', documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors, and are usually high-dimensional data. The performance of conventional multidimensional data structures(e'g', R- Tree family, K-D-B tree, grid file, TV-tree) tends to deteriorate as the number of dimensions of feature vectors increases. The R＊-tree is the most successful variant of the R-tree. In this paper, we propose a SOM-based R＊-tree as a new indexing method for high-dimensional feature vectors.The SOM-based R＊-tree combines SOM and R＊-tree to achieve search performance more scalable to high dimensionalities. Self-Organizing Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two dimensional space. The mapping preserves the topology of the feature vectors. The map is called a topological of the feature map, and preserves the mutual relationship (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. A best-matching-image-list. (BMIL) holds similar images that are closest to each codebook vector. In a topological feature map, there are empty nodes in which no image is classified. When we build an R＊-tree, we use codebook vectors of topological feature map which eliminates the empty nodes that cause unnecessary disk access and degrade retrieval performance. We experimentally compare the retrieval time cost of a SOM-based R＊-tree with that of an SOM and an R＊-tree using color feature vectors extracted from 40, 000 images. The result show that the SOM-based R＊-tree outperforms both the SOM and R＊-tree due to the reduction of the number of nodes required to build R＊-tree and retrieval time cost.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.147-154
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• 2006

Effective and efficient representation of color features of multiple video frames is an important vet challenging task for visual information management systems. This paper Proposes a Video Browsing Service(VBS) that provides both the video content retrieval and the video browsing by the real-time user interface on Web. For the scene segmentation and key frame extraction of video sequence, we proposes an efficient scene change detection method that combine the RGB color histogram with the X2 (Chi Square) histogram. Resulting key frames are linked by both physical and logical indexing. This system involves the video editing and retrieval function of a VCR's. Three elements that are the date, the need and the subject are used for video browsing. A Video Browsing Service is implemented with MySQL, PHP and JMF under Apache Web Server.

• Archives of design research
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• v.16 no.4
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• pp.481-488
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• 2003

A method to select the proper file formats of thumbnail images is proposed. After the experimental works for image file formats such as JPEG, GIF, and those effectiveness to the features contained in images, four features are obtained by feature extraction methods used in contents based image indexing, those are, the details, highly saturated colored area, the number of clustered color, and the amount of continuously varying hue. Also it is described the way to select the proper file format with those four features. In the thumbnail image generation experiments, 6 sample images are used, and with subjective assessment experiments, the resulted thumbnail images are shown to be consistent to the file formats chosen by human subjects, that is, favorable to human vision, which means the proposed method can be utilized as an automatic and systematic generation of thumbnail images for a lot of images on Web.

• 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.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 Korea Society of Computer and Information
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• v.14 no.1
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• pp.91-97
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• 2009

This paper proposes novel FE-CBIRS that finds best position of a cut to be retrieved based on color feature distribution in digital contents of IPTV. Conventional CBIRS have used a method that utilizes both color and shape information together to classify images, as well as a method that utilizes both feature information of the entire region and feature information of a partial region that is extracted by segmentation for searching. Also, in the algorithm, average, standard deviation and skewness values are used in case of color features for each hue, saturation and intensity values respectively. Furthermore, in case of using partial regions, only a few major colors are used and in case of shape features, the invariant moment is mainly used on the extracted partial regions. Due to these reasons, some problems have been issued in CBIRS in processing time and accuracy so far. Therefore, in order to tackle these problems, this paper proposes the FE-CBIRS that makes searching speed faster by classifying and indexing the extracted color information by each class and by using several cuts that are restricted in range as comparative images.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.417-420
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• 2006

Detection of faces on still color images with arbitrary backgrounds is attempted in this paper. The newly proposed method is invariant to arbitrary background, number of faces, scale, orientation, skin color, and illumination through the steps of color clustering, cluster scanning, sub-block processing, face area detection, and face verification. The sub-block method makes the proposed method invariant to the size and the number of faces in the image. The proposed method does not need any pre-training steps or a preliminary face database. The proposed method may be applied to areas such as security control, video and photo indexing, and other automatic computer vision-related fields.

• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.93-100
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• 2005

This study is to desist and implement an efficient microbial medical image retrieval system based on knowledge and content of them which can make use of more accurate decision on colony as doll as efficient education for new techicians. For this. re first address overall inference to set up flexible search path using rule-base in order U redure time required original microbial identification by searching the fastest path of microbial identification phase based on heuristics knowledge. Next, we propose a color ffature gfraction mtU, which is able to extract color feature vectors of visual contents from a inn microbial image based on especially bacteria image using HSV color model. In addition, for better retrieval performance based on large microbial databases, we present an integrated indexing technique that combines with B+-tree for indexing simple attributes, inverted file structure for text medical keywords list, and scan-based filtering method for high dimensional color feature vectors. Finally. the implemented system shows the possibility to manage and retrieve the complex microbial images using knowledge and visual contents itself effectively. We expect to decrease rapidly Loaming time for elementary technicians by tell organizing knowledge of clinical fields through proposed system.

• Journal of KIISE:Databases
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• v.30 no.2
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• pp.125-133
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• 2003

In this paper we propose an efficient content-based image retrieval method using the color and wavelet based texture features. The color features are obtained from soft-color histograms of the global image and the wavelet-based texture features are obtained from the invariant moments of the high-pass sub-band through the spatial-frequency analysis of the wavelet transform. The proposed system, called a color and texture based two-step retrieval(CTBTR), is composed of two-step query operations for an efficient image retrieval. In the first-step matching operation, the color histogram features are used to filter out the dissimilar images quickly from a large image database. The second-step matching operation applies the wavelet based texture features to the retained set of images to retrieve all relevant images successfully. The experimental results show that the proposed algorithm yields more improved retrieval accuracy with computationally efficiency than the previous methods.