• Journal of the Semiconductor & Display Technology
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• v.12 no.4
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• pp.33-38
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• 2013

Image retrieval is one of the most exciting and fastest growing research fields in the area of multimedia technology. As the amount of digital contents continues to grow users are experiencing increasing difficulty in finding specific images in their image libraries. This paper proposes an efficient image descriptor which uses a local color and texture in the non-overlapped block images. To evaluate the performance of the proposed method, we assessed the retrieval efficiency in terms of ANMRR with common image dataset. The experimental trials revealed that the proposed algorithm exhibited a significant improvement in ANMRR, compared to Dominant Color Descriptor and Edge Histogram Descriptor.

• 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.

• 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.

• Journal of Korea Multimedia Society
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• v.14 no.1
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• pp.1-14
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• 2011

While the circulation of images become active, various requirements to manage increasing database are raised. The content-based technology is one of methods to satisfy these requirements. The image is represented by feature vectors extracted by various methods in the content-based technology. The global feature method insures fast matching speed because the feature vector extracted by the global feature method is formed into a standard shape. The global feature extraction methods are classified into two categories, the spatial feature extraction and statistical feature extraction. And each group is divided by what kind of information is used, color feature or gray scale feature. In this paper, we introduce various global feature extraction technologies and compare their performance by accuracy, recall-precision graph, ANMRR, feature vector size and matching time. According to the experiments, the spatial features show good performance in non-geometrical modifications, and the extraction technologies that use color and histogram feature show the best performance.

• Journal of Korea Multimedia Society
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• v.12 no.11
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• pp.1692-1700
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• 2009

This paper proposes the direction distribution of surface normal vectors as a feature descriptor of three-dimensional models. Proposed the feature descriptor handles rotation invariance using a principal component analysis(PCA) method, and performs mesh simplification to make it robust and nonsensitive against noise addition. Our method picks samples for the distribution of normal vectors to be proportional to the area of each polygon, applies weight to the normal vectors, and applies interpolation to enhance discrimination so that the information on the surface with less area may be less reflected on composing a feature descriptor. This research measures similarity between models with a L1-norm in the probability density histogram where the distances of feature descriptors are normalized. Experimental results have shown that the proposed method has improved the retrieval performance described in an average normalized modified retrieval rank(ANMRR) by about 17.2% and the retrieval performance described in a quantitative discrimination scale by 9.6%~17.5% as compared to the existing method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.72-78
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• 2006

In this paper, we present a video shot retrieval algorithm in H.264/AVC compression domain. Unlike previous standards such as MPEG-2 and 4, H.264/AVC supports a variable block size for motion compensation. Therefore, existing video retrieval algorithms exploiting the motion vectors in MPEG-2 and 4 domains are not appropriate for H.264/AVC. So, we devise a method to project motion vectors with larger than $4{\times}4$ block sizes into those for the smallest $4{\times}4$ blocks. It also uses correlations among features for the measure of similarity. Experimental results with standard videos of 10558 frames and commercial videos of 48161 frames show that the proposed method yields ANMRR less than 0.2.

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.50-60
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• 2000

In this paper, we propose a new composite feature consists of both color and shape information that are suitable for the task of character image retrieval. This approach extracts shape-based information using Zernike moments from Y image in YCbCr color space. Zernike moments can extract shape-based features that are invariant to rotation, translation, and scaling. We also extract color-based information from the DCT coefficients of Cr and Cb image. This approach is good method reflecting human visual property and is suitable for web application such as large image database system and animation because higher retrieval rate has been achieved using only 36 features. In experiment, this method is applied to 3,834 character images. We confirmed that this approach brought about excellent effect by ANMRR(Average of Normalized, Modified Retrieval Rank), which is used in the evaluation measure of MPEG-7 color descriptor and BEP(Bull's Eye Performance), which is used in evaluation measure of shape descriptor in character image retrieval.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.10C
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• pp.1007-1016
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• 2005

This paper presents a feature extraction method for shape based retrieval of 3D models. Since the feature descriptor of 3D model should be invariant to translation, rotation and scaling, it is necessary to preprocess the 3D models to represent them in a canonical coordinate system. We use the PCA(Principal Component Analysis) method to preprocess the 3D models. Also, we apply that to make a MBR(Minimum Boundary Rectangle) and a circumsphere. The proposed algorithm is as follows. We generate a circumsphere around 3D models, where radius equals 1(r=1) and locate each model in the center of the circumsphere. We produce the concentric spheres with a different radius($r_i=i/n,\;i=1,2,{\ldots},n$). After looking for meshes intersected with the concentric spheres, we compute the curvature of the meshes. We use these curvatures as the model descriptor. Experimental results numerically show the performance improvement of proposed algorithm from min. 0.1 to max. 0.6 in comparison with conventional methods by ANMRR, although our method uses .relatively small bins. This paper uses $R{^*}-tree$ as the indexing.

• 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 IEEK Conference
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• 2001.09a
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• pp.641-644
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• 2001

멀티미디어 검색을 위한 MPEG-7 표준화 작업이 완료되어감에 따라, 멀티미디어 특징 기술자를 활용한 다양한 응용들이 나타나고 있다. 본 논문에서는 미키 마우스, 포켓 몬스터 또는 호돌이와 같은 지적 재산 정보인 동시에 고부가가치 대상인 캐릭터 이미지를 대상으로 하여, 캐릭터 이미지 특징을 분석하고, MPEG-7 에서 정의된 컬러 기술들간의 검색 효율을 비교하여, 캐릭터 이미지에 가장 적합한 기술자를 제안한다. 캐릭터 이미지는 자연 이미지와는 달리, 질감(Texture)이나 모양 (Shape)정보에 비해, 주로 컬러 정보에 의존하며,존재하는 컬러의 수가 3-6 개 범위 내에 주로 존재하고, 컬러의 분포가 고르며, 질감 성분이 많지 않은 특징을 갖고 있다. MPEG-7 에 정의된 Dominant Color, Scalable Color, Color Layout 및 Color Structure 4 종류의 기술자를 캐릭터 이미지 특징에 맞는 기술자를 유형별로 분류된 3,834개의 이미지 셋에 적용하여, 검색 성능 평가 지수인 ANMRR(Average Normalized Modified Retrieval Rank) 를 측정하여 가장 효율적인 기술자를 정의한다.