• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.5
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• pp.1-11
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• 2007

As the availability of an image information has been significantly increasing, necessity of system that can manage an image information is increasing. Accordingly, we proposed the region-based content retrieval(CBIR) algorithm based on an efficient combination of an image segmentation, an image texture, a color feature and an image's shape and position information. As a color feature, a HSI color histogram is chosen which is known to measure spatial of colors well. We used active contour and CWT(complex wavelet transform) to perform an image segmentation and extracting an image texture. And shape and position information are obtained using Hu invariant moments in the luminance of HSI model. For efficient similarity computation, the extracted features(color histogram, Hu invariant moments, and complex wavelet transform) are combined and then precision and recall are measured. As a experimental result using DB that was supported by www.freefoto.com. the proposed image retrieval engine have 94.8% precision, 82.7% recall and can apply successfully image retrieval system.

• Journal of Korea Multimedia Society
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• v.8 no.7
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• pp.906-915
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• 2005

In this paper we propose content based image retrieval method using sequential clustering and projection information. Proposed method uses the mean of color in clustered color regions by sequential clustering and the projection information in each clustered color regions, which combines spatial information with color information in images efficiently. The experimental results showed that the proposed method retrieval efficiency improved 11.6 percent over conventional methods. In addition, the proposed method robustly tolerates large changes in appearance and shape caused by changes in viewing positions, camera zooms, etc.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.7
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• pp.323-326
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• 2002

As multimedia database and digital image libraries are enlarged, CBIR(Content Based Image Retrieval) has been getting importance for the efficient search. Generally, CBIR uses primitive features such as color, shape, texture and so on. Among various methods of CBIR, Gabor wavelet has good image retrieval performance with texture features but it has a disadvantage which does not perform well for a rotated image because of its direction oriented filter. In this paper, we propose a new method to solve this problem by modifying Gabor filter for all directions. And then we will compare the searching performance of the proposed method with those of conventional image retrieval methods through experiments with trademarks.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.3
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• pp.91-108
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• 1998

Automated Storage and Retrieval Systems (AS/RSs) are an important facility for modern material management. The expected benefits of these capital-intensive facilities are gained when their control policies and their physical design parameters are determined simultaneously. In this paper we present several analytical models that capture the impact of the storage assignment policy and of the rack design on the expected storage and retrieval times. Sequential and interleaved service modes are considered for sequencing the storage and retrieval requests. We further investigate the impact of the rack structure on the relative performance of the following storage assignment policies : closest open location (random), full turnover-based policy, and class-based. Our analysis clearly indicates that significant savings in crane travel time are realized when implementing full turnover-based policy, rather than random. These savings become more and more pronounced as the profile of the storage racks approaches the square-in-time shape. Furthermore, it is shown that a class-based policy, with a small number of storage classes, will capture most of these savings and be easier to manage in practice.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.2
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• pp.9-15
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• 2000

In this paper, we proposed a content-based image retrieval method using color and object's complexity for indexing of image database. Generally, the retrieval methods using color feature can not sufficiently include the spatial information in the image. So they are reduced retrieval efficiency. Then we combined object's complexity which extracted from chain code and the conventional color feature. As a result, experiments shooed that the proposed method which considers the shape feature improved performance in conducting content-based search.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2359-2376
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• 2022

With the exponential growth of medical image big data represented by high-resolution CT images(CTI), the high-resolution CTI data is of great importance for clinical research and diagnosis. The paper takes lung CTI as an example to study. Retrieving answer CTIs similar to the input one from the large-scale lung CTI database can effectively assist physicians to diagnose. Compared with the conventional content-based image retrieval(CBIR) methods, the CBIR for lung CTIs demands higher retrieval accuracy in both the contour shape and the internal details of the organ. In traditional supervised deep learning networks, the learning of the network relies on the labeling of CTIs which is a very time-consuming task. To address this issue, the paper proposes a Weakly Supervised Similarity Evaluation Network (WSSENet) for efficiently support similarity analysis of lung CTIs. We conducted extensive experiments to verify the effectiveness of the WSSENet based on which the CBIR is performed.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.33-40
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• 2023

The mainstay of current image recovery frameworks is Content-Based Image Retrieval (CBIR). The most distinctive retrieval method involves the submission of an image query, after which the system extracts visual characteristics such as shape, color, and texture from the images. Most of the techniques use RGB color space to extract and classify images as it is the default color space of the images when those techniques fail to change the color space of the images. To determine the most effective color space for retrieving images, this research discusses the transformation of RGB to different color spaces, feature extraction, and usage of Convolutional Neural Networks for retrieval.

• Journal of KIISE:Databases
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• v.30 no.2
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• pp.145-156
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• 2003

Most of the content-based image retrieval systems focuses on similarity-based retrieval of natural picture images by utilizing color. shape, and texture features. For the neuroscience image databases, we found that retrieving similar images based on global average features is meaningless to pathological researchers. To realize the practical content-based retrieval on images in neuroscience databases, it is essential to represent internal contents or semantics of images in detail. In this paper, we present how to represent image contents and their related concepts to support more useful retrieval on such images. We also describe the operational semantics to support these advanced retrievals by using object-oriented message path expressions. Our schemes are flexible and extensible, enabling users to incrementally add more semantics on image contents for more enhanced content searching.

• Journal of Integrative Natural Science
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• v.12 no.2
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• pp.35-43
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• 2019

In the content-based image search properties, form information is simple because only the contours of objects are available, and although it can effectively extract the characteristics of the objects, it is sensitive to external noise. The radial distortion, one of these noises, is most prominent in the eyewear and, due to the structural characteristics of the imaging equipment, radiative distortion occurs in almost all imaging equipment. It is very important to determine the similarity of the objects in the images in which these distortions occurred to the actual objects. In order to improve this problem, we propose a strong image search technique for formative noise and radiative distortion using regularization phase angles and moments. Through simulation using Wang DB, the proposed algorithm proved excellent performance for radiation distortion that occurs in general. In addition, a system optimized for database can be implemented by making appropriate changes to the threshold values, enabling image retrieval with the desired level of confidence in various systems. The algorithm proposed in this paper is expected to be utilized as an optimal imaging system by extracting morphological form information of multimedia data.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.2
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• pp.131-139
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• 2003

Shape descriptors play an important role in systems for object recognition, retrieval, registration, and analysis. Seven well-known descriptors including MPEG-7 visual descriptors arebriefly reviewed and a new robust pattern recognition descriptor is proposed. Performance comparison among descriptors are presented. Experiments show that the newly proposed descriptor yields better performance results than Fourier, invariant moment, and edge histogram descriptors.