• Journal of Broadcast Engineering
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• v.18 no.3
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• pp.372-379
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• 2013

In this paper, we proposed a content-based image retrieval algorithm using local binary patterns and HSV color histogram. Images are retrieved using image input in image retrieval system. Many researches are based on global feature distribution such as color, texture and shape. These techniques decrease the retrieval performance in images which contained background the large amount of image. To overcome this drawback, the proposed method extract background fast and emphasize the feature of object by shrinking the background. The proposed method uses HSV color histogram and Local Binary Patterns. We also extract the Local Binary Patterns in quantized Hue domain. Experimental results show that the proposed method 82% precision using Corel 1000 database.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.55-59
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• 2008

For effective mobility control of UGV(unmanned ground vehicle), the terrain cover classification is an important component as well as terrain geometry recognition and obstacle detection. The vision based terrain cover classification algorithm consists of pre-processing, feature extraction, classification and post-processing. In this paper, we present a method to classify terrain covers based on the color and texture information. The color space conversion is performed for the pre-processing, the wavelet transform is applied for feature extraction, and the SVM(support vector machine) is applied for the classifier. Experimental results show that the proposed algorithm has a promising classification performance.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.363-366
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• 2000

이미지의 한가지 특징(feature)만을 고려한 내용 기반 이미지 검색(content-based image retrieval)은 두가지 이상의 특징 정보를 사용했을 경우와 비교하여 정확도(precision)가 떨어져 성능을 저하시킬 수 있다 따라서 대부분의 검색 시스템에서는 색상(color)이나 형태(shape), 질감(texture) 등과 같은 이미지의 다양한 특징들을 결합하여 검색에 이용하고 있다. 본 논문에서는 이미지의 색상 및 형태 정보를 이용하여 사용자의 질의와 유사한 이미지를 제공하고, 고 차원화된 이미지의 특징들을 클러스터링(clustering) 방법을 이용하여 빠르게 검색할 수 있도록 하였으며, 또한 검색시 그룹 경계 보정 방법을 이용하여 전체 검색을 하지 않고도 전체검색 결과와 동일한 결과를 얻을 수 있는 시스템을 설계 및 구현하였다. 실험에 사용된 데이터는 2022개의 자연 영상이였으며, HSI 색상 정보와 이미지의 에지(edge) 정보를 특징 벡터로 삼았다. 실험 결과, 색상 정보 하나만을 사용한 경우보다 정확도와 재현율면에서 사용자가 원하는 이미지와 보다 유사한 결과를 검출할 수 있었을 뿐만 아니라 클러스터링을 사용함으로써 보다 빠르고, 전체검색 결과와 동일한 검색이 가능하다는 것을 입증하였다.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1188-1193
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• 2013

In content based image retrieval (CBIR) system, the images are represented based upon its feature such as color, texture, shape, and spatial relationship etc. In this paper, we propose a MRI Image Retrieval using wavelet transform with mahalanobis distance measurement. Wavelet transformation can also be easily extended to 2-D (image) or 3-D (volume) data by successively applying 1-D transformation on different dimensions. The proposed algorithm has tested using wavelet transform and performance analysis have done with HH and $H^*$ elimination methods. The retrieval image is the relevance between a query image and any database image, the relevance similarity is ranked according to the closest similar measures computed by the mahalanobis distance measurement. An adaptive similarity synthesis approach based on a linear combination of individual feature level similarities are analyzed and presented in this paper. The feature weights are calculated by considering both the precision and recall rate of the top retrieved relevant images as predicted by our enhanced technique. Hence, to produce effective results the weights are dynamically updated for robust searching process. The experimental results show that the proposed algorithm is easily identifies target object and reduces the influence of background in the image and thus improves the performance of MRI image retrieval.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.585-589
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• 2002

In this paper, hierarchical retrieval system based on efficient feature extraction is proposed. In order to retrieval the image with robustness for geometrical transformation such as translation, scaling, and rotation. After performing the 2-level wavelet transform on image, We extract moment in low-level subband which was subdivided into subimages and texture feature, contrast of GLCM(Gray Level Co-occurrence Matrix). At first we retrieve the candidate images in database by the ones of image. To perform a more accurate image retrieval, the edge information on the high-level subband was subdivided horizontally, vertically and diagonally. And then, the energy rate of edge per direction was determined and used to compare the energy rate of edge between images for higher accuracy.

• The Research Journal of the Costume Culture
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• v.4 no.2
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• pp.247-263
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• 1996

This study focuses on a comparative study of 1960s'retro look mode in terms of the past and present in order to find out in detail how it in the past is readjusted after it was appeared in the present. For such a study, in the first place the contents of the dress and its ornament of a retro-look fashion was refined through some literature, and then, some works of the dress and its ornament of a retro-look fashion was refinded through some literature, and then, some works of eh dress and its ornament revived in 1960's mode were analysed, based on some fashion magazines at home and abroad like Bazaar, Fashion etc News in 1990s. After 1960s'retro-look mode which reappeared in 1990s was researched in terms of silhouette, detail, texture, color, and pattern, differences between those tow periods of 1960s and 1990s and their causes are summarized as follows: 1. In the aspect of silhouette, it appears that the silhouette in 1960s is that of somewhat stiff, charming image in which Body is excluded and the silhouette in 1990s is that of an soft, feminie image in which Body is emphasized. It was understood that the cause of such a delicate difference comes from the influences of the change in aesthetic senses or awareness, naturalism and neo-feminism. 2. In the aspect of detail, it appears that the detail in 1990s is of an attempt to express in diverse images, compared to that in 1960s, and new images are created new image in 1990s by means of presenting entirely ill-matched images. The major cause of that is because of Antistandard fashion. 3. In the aspect of textures, it appears that a great feature is that the texture in 1990s is of that introduced, being changed in natural and high-class looks, compared to that of 1960s. It was reviewed that the major cause of this is because of a result from the influence of naturalism and the technical growth in various fields which has brought the development of dress material. 4. In the aspect of color, it appears that the color in 1990s is of an image of primary color which is far more sensual and feminie than that of 1960s. It was studied that the major cause of ti comes from the influence of neo-feminism, etc. 5. In the aspect of pattern, it appears that the pattern in 1990s is of that of symbolism, transposition, and the ecletic feature of various modes which appear more deeply than that of 1960s. It was studied that the major cause of such changes is because of a trend of postmodernism which has brought the change of the spiritual structure different from that in the age of modernism. In conclusion, it was understood that the retro-look fashion is of an expression technic of dress and its ornament in that o dress in the pst is simply imitate, but new reconstitution is done by using the elements in the past. at the same time, ti was clarified that even though the elements in the past are revived as they were, dress and its ornament is governed by the social and cultural environments of he day, and with this proof it can be said that the fashion in each age is of a reflection of social phenomena of that age.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.133-139
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• 2006

Multimedia data is increasing rapidly by development of computer Information technology. Specially, quick and accurate processing of image data is required in image retrieval field. But it is difficult to guarantee both quickness and accuracy. This article suggests the algorithm that extracts representative features of image using genetic algorithm to solve this problem. This algorithm guarantees quickness and accuracy of retrieval by extracting representative features of image. We used color and texture as feature of image. Experiment shows that feature extracting method that is proposed is more accurate than existing study. So this study establishes propriety of method that is proposed.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.502-507
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• 2009

We present a real-time photomosaics method for small image set optimized by feature selection method. Photomosaics is an image that is divided into cells (usually rectangular grids), each of which is replaced with another image of appropriate color, shape and texture pattern. This method needs large set of tile images which have various types of image pattern. But large amount of photo images requires high cost for pattern searching and large space for saving the images. These requirements can cause problems in the application to a real-time domain or mobile devices with limited resources. Our approach is a genetic feature selection method for building an optimized image set to accelerate pattern searching speed and minimize the memory cost.

• Journal of Korea Multimedia Society
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• v.7 no.7
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• pp.877-885
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• 2004

In this paper, we propose an enhancement shot change detection method using the neural net and the robust feature selection out of multiple features. The previous shot change detection methods usually used single feature and fixed threshold between consecutive frames. However, contents such as color, shape, background, and texture change simultaneously at shot change points in a video sequence. Therefore, in this paper, we detect the shot changes effectively using robust features, which are supplementary each other, rather than using single feature. In this paper, we use the typical CART (classification and regression tree) of data mining method to select the robust features, and the backpropagation neural net to determine the threshold of the each selected features. And to evaluation the performance of the robust feature selection, we compare the proposed method to the PCA(principal component analysis) method of the typical feature selection. According to the experimental result. it was revealed that the performance of our method had better that than the PCA method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.990-999
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• 2007

In this paper, we propose the region-based image retrieval method using JSEG which is a method for unsupervised segmentation of color-texture regions. JSEG is an algorithm that discretizes an image by color classification, makes the J-image by applying a region to window mask, and then segments the image by using a region growing and merging. The segmented image from JSEG is given to a user as the query image, and a user can select a few segmented regions as the query region. After finding the MBR of regions selected by user query and generating the multiple window masks based on the center point of MBR, we extract the feature vectors from selected regions. We use the accumulated histogram as the global descriptor for performance comparison of extracted feature vectors in each method. Our approach fast and accurately supplies the relevant images for the given query, as the feature vectors extracted from specific regions and global regions are simultaneously applied to image retrieval. Experimental evidence suggests that our algorithm outperforms the recent image-based methods for image indexing and retrieval.