• Journal of KIISE:Software and Applications
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• v.29 no.12
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• pp.940-946
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• 2002

This paper proposes an image retrieval method to use the modified color correlogram. For retrieving images with less effect of the size variation of the regions in an image, the modified color correlogram is extracted by normalizing auto-correlogram and cross-correlogram of the color correlogram from a color image, and the similarity of two images is calculated by putting the less weight to the auto-correlogram of the modified color correlogram. Because proposed method uses the information of the color correlogram more effectively, we can get better results than that of color correlogram method. In the experiments, the performance of the proposed method is better as compared with that of the color cerrelogram method.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.753-758
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• 2002

This paper proposes the color Cross-correlogram and its extraction method for efficient image retrieval. Color cross-correlogram represents the probability that different colors are existed at any two pixels whose distance is fixed in an image. Color cross-correlogram doesn't have the information about the region size that has a color, so color cross-correlogram can have good performance in retrieving images that have different size color regions. The experiments say that we can get the good retrieval results in the images that have various size color regions, and get the better retrieval results when using color cross-correlogram than those of retrieval using color correlogram.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.5C
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• pp.513-519
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• 2007

Color correlogram is widely used in content-based image retrieval (CBIR) because it extracts not only the color distribution of pixels in images like color histogram, but also extracts the spatial information of pixels in the images. The color correlogram uses single color space. Therefore, the color correlograms does not have robust discriminative features. In this paper, we use both RGB and HSV color spaces together for the color correlogram to achieve better discriminative features. The proposed algorithm is tested on a large database of images and the results are compared with the single color space color correlogram. In simulation results, the proposed algorithm 5.63 average retrieval rank less than single color space correlogram.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.153-156
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• 2000

Recently, there has been studied on feature extraction method for efficient content-based image retrieval. Especially, Many researchers have been studying on extracting feature from color Information, because of its advantages. This paper proposes a feature and its extraction method based on color correlogram that is extracted from color information in an image. the proposed method is computed from the image segmented into two parts; the complex part and the plain part. Our experiments show that the performance of the proposed method is better as compared with that of the original color correlogram method.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.9-11
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• 2005

We propose a new content-based image retrieval using a block color co-occurrence matrix (BCCM) and pattern correlogram. In the proposed method, the color feature vectors are extracted by using BCCM that represents the probability of the co-occurrence of two mean colors within blocks. Also the pattern feature vectors are extracted by using pattern correlogram which is combined with spatial correlation of pattern. In the proposed pattern correlogram method. after block-divided image is classified into 48 patterns with respect to the change of the RGB color of the image, joint probability between the same pattern from the surrounding blocks existing at the fixed distance and the center pattern is calculated. Experimental results show that the proposed method can outperform the conventional methods as regards the precision and the size of the feature vector dimension.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.2
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• pp.1-8
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• 2003

This paper proposes an object-based image retrieval technique based on the dominant color pair information. Most of existing methods for content based retrieval extract the features from an image as a whole, instead of an object of interest. As a result, the retrieval performance tends to degrade due to the background colors. This paper proposes an object based retrieval scheme, in which an object of interest is used as a query and the similarity is measured on candidate regions of DB images where the object may exist. From the segmented image, the dominant color pair information between adjacent regions is used for selecting candidate regions. The similarity between the query image and DB image is measured by using the color correlogram technique. The dominant color pair information is robust against translation, rotation, and scaling. Experimental results show that the performance of the proposed method has been improved by reducing the errors caused by background colors.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.4
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• pp.346-352
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• 2011

This paper presents a system of road extraction for traffic images from a single camera. The road in the images is subject to large changes in appearance because of environmental effects. The proposed system is based on the integration of color correlograms and random forest. The color correlogram depicts the color properties of an image properly. Using the random forest, road extraction is formulated as a learning paradigm. The combined effects of color correlograms and random forest create a robust system capable of extracting the road in very changeable situations.

• Journal of the Semiconductor & Display Technology
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• v.11 no.4
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• pp.49-55
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• 2012

This paper proposes an efficient method of object identification and localization for image recognition. The new proposed algorithm utilizes correlogram back-projection in the YCbCr chromaticity components to handle the problem of sub-region querying. Utilizing similar spatial color information enables users to detect and locate primary location and candidate regions accurately, without the need for additional information about the number of objects. Comparing this proposed algorithm to existing methods, experimental results show that improvement of 21% was observed. These results reveal that color correlogram is markedly more effective than color histogram for this task. Main contribution of this paper is that a different way of treating color spaces and a histogram measure, which involves information on spatial color, are applied in object localization. This approach opens up new opportunities for object detection for the use in the area of interactive image and 2-D based augmented reality.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.1-10
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• 2005

Content-based image retrieval systems are being actively investigated owing to their ability to retrieve images based on the actual visual content rather than by manually associated textual descriptions. In this paper, we propose a novel approach for image retrieval based on edge structural features using edge correlogram and color coherence vector. After color vector angle is applied in the pre-processing stage, an image is divided into two image parts (high frequency image and low frequency image). In low frequency image, the global color distribution of smooth pixels is extracted by color coherence vector, thereby incorporating spatial information into the proposed color descriptor. Meanwhile, in high frequency image, the distribution of the gray pairs at an edge is extracted by edge correlogram. Since the proposed algorithm includes the spatial and edge information between colors, it can robustly reduce the effect of the significant change in appearance and shape in image analysis. The proposed method provides a simple and flexible description for the image with complex scene in terms of structural features of the image contents. Experimental evidence suggests that our algorithm outperforms the recently histogram refinement methods for image indexing and retrieval. To index the multidimensional feature vectors, we use R*-tree structure.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.1
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• pp.10-16
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• 2007

In this paper, we propose a technique for retrieving images using spatial color correlation and texture characteristics based on local fourier transform. In order to retrieve images, two new descriptors are proposed. One is a color descriptor which represents spatial color correlation. The other is a descriptor combining the proposed color descriptor with texture descriptor. Since most of existing color descriptors including color correlogram which represent spatial color correlation considered just color distribution between neighborhood pixels, the structural information of neighborhood pixels is not considered. Therefore, a novel color descriptor which simultaneously represents spatial color distribution and structural information is proposed. The proposed color descriptor represents color distribution of Min-Max color pairs calculating color distance between center pixel and neighborhood pixels in a block with 3x3 size. Also, the structural information which indicates directional difference between minimum color and maximum color is simultaneously considered. Then new color descriptor(min-max color correlation descriptor, MMCCD) containing mean and variance values of each directional difference is generated. While the proposed color descriptor includes by far smaller feature vector over color correlogram, the proposed color descriptor improves 2.5 % ${\sim}$ 13.21% precision rate, compared with color correlogram. In addition, we propose a another descriptor which combines the proposed color descriptor and texture characteristics based on local fourier transform. The combined method reduces size of feature vector as well as shows improved results over existing methods.