• Journal of KIISE:Databases
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• v.35 no.5
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• pp.379-390
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• 2008

Due to the increase of the amount of image data, the demand for searching similar images is continuously increasing. Therefore, many researches about the content-based image retrieval (CBIR) are conducted to search similar images effectively. In CBIR, it uses image contents such as color, shape, and texture for more effective retrieval. However, when we apply CBIR to satellite images which are complex and pose the difficulty in using color information, we can have trouble to get a good retrieval result. Since it is difficult to use color information of satellite images, we need image segmentation to use shape information by separating the shape of an object in a satellite image. However, because satellite images are complex, image segmentation is hard and poor image segmentation results in poor retrieval results. In this paper, we propose a new approach to search similar images without image segmentation for satellite images. To do a similarity search without image segmentation, we define a similarity of an image by considering SIFT keypoint descriptors which doesn't require image segmentation. Experimental results show that the proposed approach more effectively searches similar satellite images which are complex and pose the difficulty in using color information.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.1
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• pp.63-69
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• 2011

Recent growth of the geospatial information on the web made it possible to applicate spatial data. Also, the demand for rich and latest information shows a steady growth. The need for the new service using conflation of the existing spatial databases is on the increase. The information delivery of the services using the road vector and aerial image is reached intuitionally and accurately. However, the spatial inconsistencies in map services such as Daum map, Naver map and Google map is the problem. Our approach is processed to extract the road candidate image, match the template and filter the control points pair using vector median. Finally, CNS node and link are aligned to the real road with the aerial image. The experimental results show that our approach can align a set of CNS node and link with aerial imagery for daejon, such that the completeness and correctness of the aligned road have improved about 35% compare with the original roads.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2938-2956
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• 2020

Objective image quality assessment (IQA) models have been developed by effective features to imitate the characteristics of human visual system (HVS). Actually, HVS is extremely sensitive to color degradation and complex texture changes. In this paper, we firstly reveal that many existing full reference image quality assessment (FR-IQA) methods can hardly measure the image quality with contrast and masking texture changes. To solve this problem, considering texture masking effect, we proposed a novel FR-IQA method, called Texture and Color Quality Index (TCQI). The proposed method considers both in the masking effect texture and color visual perceptual threshold, which adopts three kinds of features to reflect masking texture, color difference and structural information. Furthermore, random forest (RF) is used to address the drawbacks of existing pooling technologies. Compared with other traditional learning-based tools (support vector regression and neural network), RF can achieve the better prediction performance. Experiments conducted on five large-scale databases demonstrate that our approach is highly consistent with subjective perception, outperforms twelve the state-of-the-art IQA models in terms of prediction accuracy and keeps a moderate computational complexity. The cross database validation also validates our approach achieves the ability to maintain high robustness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1712-1731
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• 2016

In this paper, we propose a novel saliency detection framework via multiple random walks (MRW) which simulate multiple agents on a graph simultaneously. In the MRW system, two agents, which represent the seeds of background and foreground, traverse the graph according to a transition matrix, and interact with each other to achieve a state of equilibrium. The proposed algorithm is divided into three steps. First, an initial segmentation is performed to partition an input image into homogeneous regions (i.e., superpixels) for saliency computation. Based on the regions of image, we construct a graph that the nodes correspond to the superpixels in the image, and the edges between neighboring nodes represent the similarities of the corresponding superpixels. Second, to generate the seeds of background, we first filter out one of the four boundaries that most unlikely belong to the background. The superpixels on each of the three remaining sides of the image will be labeled as the seeds of background. To generate the seeds of foreground, we utilize the center prior that foreground objects tend to appear near the image center. In last step, the seeds of foreground and background are treated as two different agents in multiple random walkers to complete the process of salient object detection. Experimental results on three benchmark databases demonstrate the proposed method performs well when it against the state-of-the-art methods in terms of accuracy and robustness.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.5C
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• pp.496-512
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• 2007

In this paper, we propose a novel JPEG-2000 compressed image retrieval system using feature vector extracted through partial entropy decoding. Main idea of the proposed method is to utilize the context information that is generated during entropy encoding/decoding. In the framework of JPEG-2000, the context of a current coefficient is determined depending on the pattern of the significance and/or the sign of its neighbors in three bit-plane coding passes and four coding modes. The contexts provide a model for estimating the probability of each symbol to be coded. And they can efficiently describe texture images which have different pattern because they represent the local property of images. In addition, our system can directly search the images in the JPEG-2000 compressed domain without full decompression. Therefore, our proposed scheme can accelerate the work of retrieving images. We create various distortion and similarity image databases using MIT VisTex texture images for simulation. we evaluate the proposed algorithm comparing with the previous ones. Through simulations, we demonstrate that our method achieves good performance in terms of the retrieval accuracy as well as the computational complexity.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.362-371
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• 2003

In this paper, we propose an optimal interest point extraction method to support shape-base image classification and indexing for image database by applying a dynamic threshold that reflects the characteristics of the shape contour. The threshold is determined dynamically by comparing the contour length ratio of the original shape and the approximated polygon while the algorithm is running. Because our algorithm considers the characteristics of the shape contour, it can minimize the number of interest points. For n points of the contour, the proposed algorithm has O(nlogn) computational cost on an average to extract the number of m optimal interest points. Experiments were performed on the 70 synthetic shapes of 7 different contour types and 1100 fish shapes. It shows the average optimization ratio up to 0.92 and has 14% improvement, compared to the fixed threshold method. The shape features extracted from our proposed method can be used for shape-based image classification, indexing, and similarity search via normalization.

• Journal of KIISE:Databases
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• v.36 no.6
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• pp.446-454
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• 2009

Among various region detector and shape feature extraction method, MSER(Maximally Stable Extremal Region) and SIFT and its variant methods are popularly used in computer vision application. However, since SIFT is sensitive to the illumination change and MSER is sensitive to the scale change, it is not easy to apply the image similarity retrieval. In this paper, we present a Scale and Rotation Invariant Region Feature(SRIRF) descriptor using scale pyramid, MSER and affine normalization. The proposed SRIRF method is robust to scale, rotation, illumination change of image since it uses the affine normalization and the scale pyramid. We have tested the SRIRF method on various images. Experimental results demonstrate that the retrieval performance of the SRIRF method is about 20%, 38%, 11%, 24% better than those of traditional SIFT, PCA-SIFT, CE-SIFT and SURF, respectively.

• Journal of KIISE:Databases
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• v.34 no.2
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• pp.99-108
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• 2007

In this paper, we design and implement the Film Defect Inspection System (FDIS) that detects film defects and determines their types which can be used for producing polarized films of TFT-LCD. The proposed system is designed to detect film defects from polarized film images using image segmentation techniques and to determine defect types through the image analysis of detected defects. To determine defect types, we extract features such as shape and texture of defects, and compare those features with corresponding features of referential images stored in a template database. Experimental results using FDIS show that the proposed system detects all defects of test images effectively (Precision 1.0, Recall 1.0) and efficiently (within 0.64 second in average), and achieves the considerably high correctness in determining defect types (Precision 0.96 and Recall 0.95 in average). In addition, our system shows the high robustness for rotated transformation of images, achieving Precision 0.95 and Recall 0.89 in average.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.324-333
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• 2001

In this paper we present a novel color image segmentation algorithm based on a Gaussian Mixture Model(GMM). It is introduced a Deterministic Annealing Expectation Maximization(DAEM) algorithm which is developed using the principle of maximum entropy to overcome the local maxima problem associated with the standard EM algorithm. In our approach, the GMM is used to represent the multi-colored objects statistically and its parameters are estimated by DAEM algorithm. We also develop the automatic determination method of the number of components in Gaussian mixtures models. The segmentation of image is based on the maximum posterior probability distribution which is calculated by using the GMM. The experimental results show that the proposed DAEM can estimate the parameters more accurately than the standard EM and the determination method of the number of mixture models is very efficient. When tested on two natural images, the proposed algorithm performs much better than the traditional algorithm in segmenting the image fields.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.4
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• pp.317-323
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• 2001

DICOM(Digital Imaging and Communications in MediCine), standard of medical image operation, present the methods for communications and Storage of Medical Image. medical image acquired from patient in hospital made DICOM files. this paper purposes design and implementation methodologies of a web-based medical information system that consists of DICOM (Digital Imaging and Communications in Medicine) databases and functional components of a web server in order to support the access of medical information with Intemet web browser. we store the patient and image information to database using reading the group and element oJ DICOM file. we made file transfer module by implementing DICOM Store service, in result, we can transfer DICOM file to IF based host or computer. We compose web component of communications and Storage service, user be used DICOM Service by web Browser.