• Journal of information and communication convergence engineering
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• v.2 no.2
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• pp.123-127
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• 2004

The most popular technique for image retrieval in a heterogeneous collection of color images is the comparison of images based on their color histogram. The color histogram describes the distribution of colors in the color space of a color image. In the most image retrieval systems, the color histogram is used to compute similarities between the query image and all the images in a database. But, small changes in the resolution, scaling, and illumination may cause important modifications of the color histogram, and so two color images may be considered to be very different from each other even though they have completely related semantics. A new method of color feature representation based on the 3-dimensional RGB color map is proposed to improve the defects of the color histogram. The proposed method is based on the three 2-dimensional projection map evaluated by projecting the RGB color space on the RG, GB, and BR surfaces. The experimental results reveal that the proposed is less sensitive to small changes in the scene and that achieve higher retrieval performances than the traditional color histogram.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1771-1774
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• 2003

Image segmentation is very important technique as preprocessing. It is used for various applications such as object recognition, computer vision, object based image compression. In this paper, a method which segments the multidimensional image using a hierarchical histogram approach, is proposed. The hierarchical histogram approach is a method that decomposes the multi-dimensional situation into multi levels of 1 dimensional situations. It has the advantage of the rapid and easy calculation of the histogram, and at the same time because the histogram is applied at each level and not as a whole, it is possible to have more detailed partitioning of the situation.

• Journal of Intelligence and Information Systems
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• v.10 no.1
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• pp.1-11
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• 2004

It is very important part of pre-processing for get better results by image processing that get emphasized image by processing of source image. Emphasized image is not only good looking image but clear and sharp image. Emphasized images are used very useful data at contour extraction and image recognition in image processing. It have different image recognition by how much represent a origin scene in row quality image. Present algorithms that get emphasized premier image do not get clear picture of degree that want in various kind of images and there is shortcoming that need much process times being proportional size of picture quality or accumulation degree of histogram. In this paper, we propose method to change distribution chart that pixels occupy in histogram as subsequentness in reflex of various kinds as well as that picture quality reflex method to emphasize so that is suitable in practical use purpose originally of premier. Proposed algorithm re-allot histogram distribution by reverse tracking histogram. Experimental images are same result and take less processing time than histogram equalization.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.194-197
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• 2000

Wavelet transform used for content-based image retrieval has good performance in texture image. Image features for content-based image retrieval are color, texture, and shape. In this paper, we use color feature extracted from HSI color space known as most similar vision system to human vision system and texture feature extracted from wavelet histogram which has multiresolution property. Proposed method is compared with HSI color histogram method and wavelet histogram method. It is shown better performance.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.867-871
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• 2004

In this paper, a new analytic approach for shape preserving contrast enhancement is presented. Contrast enhancement is achieved by means of segmental histogram stretching modification which preserves the given image shape, not distorting the original shape. After global stretching, the image is partitioned into several level-sets according to threshold condition. The image information of each level-set is represented as typical value based on grouped differential values. The basic property is modified into common local schemes, thereby introducing the enhanced effect through extreme discrimination between subsets. The scheme is based on stretching the histogram of subsets in which the intensity gray levels between connected pixels are approximately same In spite of histogram widening, stretched by local image information, it neither creates nor destroys the original image, thereby preserving image shape and enhancing the contrast. By designing local histogram stretching operations, we can preserve the original shape of level-sets of the image, and also enhance the global intensity. Thus it can hold the main properties of both global and local image schemes, which leads to versatile applications in the field of digital epigraphy.

• Journal of the Semiconductor & Display Technology
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• v.14 no.4
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• pp.50-55
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• 2015

This paper proposes a new histogram partition stretching and shrinking method for infrared image enhancement. The proposed method divides the histogram of an input image into three partitions according to its mean value and standard deviation. The method stretches both the dark partition and the bright partition of the histogram, while it shrinks the medium partition. As the result, both the dark part and the bright part of the image have more brightness levels. The proposed method is implemented using C language in an embedded Linux system for a high-speed real-time image processing. Experiments were conducted by using various infrared images. The results show that the proposed algorithm is successful for the infrared image enhancement.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.4
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• pp.239-244
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• 2009

Image contrast enhancement has an important role in image processing applications. Conventional contrast enhancement techniques, histogram stretching and histogram equalization, and many methods based on histogram equalization often fail to produce satisfactory results for broad variety of low-contrast images. So, this paper proposes a new image contrast enhancement method based on the clustering method. The number of cluster of histogram is found by analysing the histogram of original image. The histogram components is classified using K-means algorithm. And then these histogram components are performed histogram stretching and histogram equalization selectively by comparing cluster range with pixel rate of cluster. From the expremental results, the proposed method was more effective than conventional contrast enhancement techniques.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.5
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• pp.889-898
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• 1997

Most general content-based image retrieval techniques use color and texture as retrieval indices. Spatial information is not used to color histogram and color pair based on color retrieval techniques. This paper proposes the selection of a set of representative in the duplicated color histogram, the analysis of spatial information of the selected colors and the image retrieval process based on the duplicated color histogram and spatial information. Two color historgrams for background and object are used in order to decide on color selection in the duplicated color histogram. Spatial information is obtained using a maximum entropy discretization. A retrieval process applies to duplicated color histogram and spatial to retrieve input images and relevant images. As the result of experiment of the image retrieval, improved color his togram and spatial information method hs increased the retrieval effectiveness more the color histogram method and color pair method.

• ETRI Journal
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• v.24 no.1
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• pp.23-30
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• 2002

MPEG-7 Visual Standard specifies a set of descriptors that can be used to measure similarity in images or video. Among them, the Edge Histogram Descriptor describes edge distribution with a histogram based on local edge distribution in an image. Since the Edge Histogram Descriptor recommended for the MPEG-7 standard represents only local edge distribution in the image, the matching performance for image retrieval may not be satisfactory. This paper proposes the use of global and semi-local edge histograms generated directly from the local histogram bins to increase the matching performance. Then, the global, semi-global, and local histograms of images are combined to measure the image similarity and are compared with the MPEG-7 descriptor of the local-only histogram. Since we exploit the absolute location of the edge in the image as well as its global composition, the proposed matching method can retrieve semantically similar images. Experiments on MPEG-7 test images show that the proposed method yields better retrieval performance by an amount of 0.04 in ANMRR, which shows a significant difference in visual inspection.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2008.11a
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• pp.15-19
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• 2008

Image hash는 영상에서 유사성을 찾는 방법으로 사용될 수 있는 기술자(Descriptor)로 특징지을 수 있다. 많은 image hash 방법중에 Histogram-based image hash는 Histogram equalization을 제외한 보통 잡음 및 다양한 기하학적 변조를 주어도 같은 그림을 찾아내는데 강력한 기능을 수행한다. 본 논문에서는 Histogram-Based Hash를 생성함에 있어 서로 다른 3개의 bin의 관계를 이용하여 Hash를 생성하였다. 본 논문은 이를 통해 영상의 유사성을 찾아내는데 있어 원본영상에 대해 기하학적 변조뿐만 아니라 상대적으로 성능이 약했던 Histogram equalization을 이용한 변조에 대해서도 성능이 개선되었다. 또한 가우시안 필터링의 알파 값을 다르게 지정함으로 인하여 생성되는 두 히스토그램을 이용하여 기존의 방법보다 성능이 개선되었다.