• 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 the Korea Society of Computer and Information
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• v.5 no.3
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• pp.215-219
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• 2000

The purpose of this paper is to propose a method to maximize the efficiency of a content-based image retrieval for various kinds of images. This paper discuss the self-adaptivity for the change of image domain and the self-selection of optimal features for query image, and present the efficient method to maximize content-based retrieval for various kinds of images. In this method, a content-based retrieval system is adopted to select automatically distinctive feature patterns which have a maximum efficiency of image retrieval in various kinds of images. Experimental results show that the Proposed method is improved 3% than the method using individual features.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.479-484
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• 1998

As technology in a computer hardware and software advances, efficient information retrieval from multimedia database gets highly demanded. Recently, it has been actively exploited to retrieve information based on the stored contents. However, most of the methods emphasize on the points which are far from human intuition or emotion. In order to overcome this shortcoming , this paper attempts to apply interactive genetic algorithm to content-based image retrieval. A preliminary result with subjective test shows the usefulness of this approach.

• Journal of the Semiconductor & Display Technology
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• v.6 no.2 s.19
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• pp.53-57
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• 2007

In this paper, a new content-based color image retrieval method is proposed, in which both the color content and the spatial relationship of image have been taken into account. In order to represent the spatial distribution information of image, a disorder matrix, which has the invariance to the rotation and translation of the image content, has been designed. This is based on multi-resolution color-spatial information. We present our algorithm in the following section, and then verified the search results with comparison to other methods, such as color histogram, wavelet histogram, correlogram and wavelet correlogram. Experimental results with various types of images show that the proposed method not only achieves a high image retrieval performance but also improve the retrieval precision.

• Journal of Korea Multimedia Society
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• v.9 no.5
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• pp.564-575
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• 2006

With the rapid increase of the number of multimedia contents, people consume a lot of multimedia contents through various distribution channels. Content-based image retrieval uses visual features that represent the contents of images. And users can retrieve or filter images based on the contents of the images using the features. But, the editing of the multimedia contents distorts the original visual features of the multimedia contents, thereby the performance of content-based image retrieval system could be lowered. In this paper, we describe the image editing effects that lower the performance of the retrieval system and propose algorithms that can remove the image editing effect and improve content-based image retrieval system.

• Journal of the Semiconductor & Display Technology
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• v.7 no.4
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• pp.13-18
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• 2008

This paper explores a content-based image retrieval framework with relevance feedback based on genetic algorithm (GA). This framework adopts GA to learn the user preferences using the similarity functions defined for all available descriptors. The objective of the GA-based learning methods is to learn the user preferences using the similarity functions and to find a descriptor combination function that best represents the user perception. Experiments were performed to validate the proposed frameworks. The experiments employed the natural image databases and color and texture descriptors to represent the content of database images. The proposed frameworks were compared with the other two relevance feedback methods regarding effectiveness in image retrieval tasks. Experiment results demonstrate the superiority of the proposed method.

• ETRI Journal
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• v.20 no.3
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• pp.272-283
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• 1998

In this paper, we propose efficient content-based image retrieval methods using the automatic extraction of the low-level visual features as image content. Two new feature extraction methods are presented. The first one os an advanced color feature extraction derived from the modification of Stricker's method. The second one is a texture feature extraction using some DCT coefficients which represent some dominant directions and gray level variations of the image. In the experiment with an image database of 200 natural images, the proposed methods show higher performance than other methods. They can be combined into an efficient hierarchical retrieval method.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.8
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• pp.837-841
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• 2010

This paper proposes an effective similarity measure for content-based image retrieval using MPEG-7 DCD. The proposed method can measure the similarity of images with the percentage of dominant colors extracted from images. As the result of experiments, we achieved a significant improvement of 18.92% with global DCD and 47.22% with local DCD in ANMRR than the result by QHDM. This result shows that the proposed method is an effective similarity measure for content-based image retrieval. Especially, our method is useful for region-based image retrieval.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.364-366
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• 2005

From the 90's, the image information retrieval methods have been on progress. As good examples of the methods, Conventional histogram method and merged-color histogram method were introduced. They could get good result in image retrieval. However, Conventional histogram method has disadvantages if the histogram is shifted as a result of intensity change. Merged-color histogram, also, causes more process so, it needs more time to retrieve images. In this paper, we propose an improved new method using Adaptive Color Histogram Bin Matching(AHB) in image retrieval. The proposed method has been tested and verified through a number of simulations using hundreds of images in a database. The simulation results have Quickly yielded the highly accurate candidate images in comparison to other retrieval methods. We show that AHB's can give superior results to color histograms for image retrieval.