• The Transactions of the Korea Information Processing Society
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• v.5 no.8
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• pp.1951-1959
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• 1998

In this paper, we propose an efficient feature extaetion method for content-based approach and implement an image retrieval system in the Oracle database. First, we estract color feature by the modified Stricker's method from input images, and this color feature and ART2 neural network are used for the rough classification of images. Next, we extract texture feature using wavelet transform, and finally exeute the detailed classification on the rough classified images from the previous step. Exsing the proposed feature extraction methods, we implement a useful image retrieval system by Extended SQI, statement on the relational database. The proposed system is implemented on the Oracle DBMS, and in the experimental results with 200 sample images, it shows the retrieval rate 90% and 81% in Recall and Precision, respectively.

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.10-22
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• 2000

In this paper, we present a texture description method as a standardization of multimedia contents description. Like color, shape, object and camera motion information, texture is one of very important information in the visual part of international standard (MPEG-7) in multimedia contents description. Current MPEG-7 texture descriptor has been designed to fit human visual system. Many psychophysical experiments give evidence that the brain decomposes the spectra into perceptual channels that are bands in spatial frequency. The MPEG-7 texture description method has employed Radon transform that fits with HVS behavior. By taking average energy and energy deviation of HVS channels, the texture descriptor is generated. To test the performance of current texture descriptor, experiments with MPEG-7 Texture data sets of T1 to T7 are performed. Results show that the current MPEG-7 texture descriptor gives better retrieval rate and fast and fast extraction time for texture feature.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5A
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• pp.375-387
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• 2012

This paper proposes a new real-time object tracking algorithm using particle filters with color and texture features in moving CCD camera images. If the user selects an initial object, this region is declared as a target particle and an initial state is modeled. Then, N particles are generated based on random distribution and CS-LBP (Centre Symmetric Local Binary Patterns) for texture model and weighted color distribution is modeled from each particle. For observation likelihoods estimation, Bhattacharyya distance between particles and their feature models are calculated and this observation likelihoods are used for weights of individual particles. After weights estimation, a new particle which has the maximum weight is selected and new particles are re-sampled using the maximum particle. For performance comparison, we tested a few combinations of features and particle filters. The proposed algorithm showed best object tracking performance when we used color and texture model simultaneously for likelihood estimation.

• Korean Journal of Remote Sensing
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• v.26 no.3
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• pp.287-295
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• 2010

Water level of the Tonle Sap is largely influenced by the Mekong River. During the wet season, the lacustrine landform and vegetated areas are covered with water. Change detection in this area provides information required for human activities and sustainable development around the Tonle Sap. In order to detect the changes in the Tonle Sap floodplain, fifteen ALOS-PALSAR L-band data acquired from January 2007 to January 2009 and examined in this study. Since L-band is able to penetrate into vegetation cover, it enables us to study the changes according to water level of floodplain developed in the rainforest. Four types of images were constructed and studied include 1) ratio images, 2) correlation coefficient images, 3) texture feature ratio images and 4) multi-color composite images. Change images (in each 46 day interval) extracted from the ratio images, coherence images and texture feature ratio images were formed for detecting land cover change. Two RGB images are also obtained by compositing three images acquired in the early, in the middle and at the end of the rainy season in 2007 and 2008. Combination of the methods results that the change images present the relationship between vegetation and water level, leaf fall forest as well as cultivation and harvest crop.

• Proceedings of the Korea Multimedia Society Conference
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• 1998.04a
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• pp.34-39
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• 1998

영상검색을 위해 Wavelet 변환을 사용한 특징추출 접근방법은 영상들을 압축과 동시에 인덱스 할 수 있어서 영상 데이터베이스 저장과 관리의 복잡성이 상당히 감소될 수 있다. 본 연구는 각 영상의 Hue값에 대해 위치 정보의 주파수 정보를 가지는 Wavelet 변환의 성질을 이용하여 2단계 Wavelet 변환 후 생성된 저대역 부밴드에서 칼라 특징을 추출하고 나머지 부밴드에서 질감 특징을 추출하여 영상 데이터베이스의 검색에 이용한다. 200개 영상을 사용하여 실험한 결과, 제안된 방법은 recall과 precision에서 약 97%, 81%의 검색 효율을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3806-3825
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• 2016

This paper brings out a neoteric frame of reference for visual semantic based 3d video search and retrieval applications. Newfangled 3D retrieval application spotlight on shape analysis like object matching, classification and retrieval not only sticking up entirely with video retrieval. In this ambit, we delve into 3D-CBVR (Content Based Video Retrieval) concept for the first time. For this purpose we intent to hitch on BOVW and Mapreduce in 3D framework. Here, we tried to coalesce shape, color and texture for feature extraction. For this purpose, we have used combination of geometric & topological features for shape and 3D co-occurrence matrix for color and texture. After thriving extraction of local descriptors, TB-PCT (Threshold Based- Predictive Clustering Tree) algorithm is used to generate visual codebook. Further, matching is performed using soft weighting scheme with L₂ distance function. As a final step, retrieved results are ranked according to the Index value and produce results .In order to handle prodigious amount of data and Efficacious retrieval, we have incorporated HDFS in our Intellection. Using 3D video dataset, we fiture the performance of our proposed system which can pan out that the proposed work gives meticulous result and also reduce the time intricacy.

• Journal of Digital Contents Society
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• v.10 no.3
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• pp.423-431
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• 2009

There are various image retrieval methods using shape, color and texture features. One of the most active area is using shape and color information. A number of shape representations have been suggested to recognize shapes even under affine transformation. There are many kinds of method for shape recognition, the well-known method is Fourier descriptors and moment invariant. The other method is CSS(Curvature Scale Space). The maxima of curvature scale space image have already been used to represent 2-D shapes in different applications. Because preexistence CSS exists several problems, in this paper we use improved CSS method for retrieval image. There are two kinds of method, One is using RGB color information feature and the other is using HSI color information feature. In this paper we used HSI color model to represent color histogram before, then use it as comparison measure. The similarity is measured by using Euclidean distance and for reduce search time and accuracy, We use DTW for measure similarity. Compare with the result of using Euclidean distance, we can find efficiency elevated.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.2
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• pp.12-17
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• 2003

This paper proposes content-based image retrieval system with fuzzy ART neural network algorithm. Retrieving large database of image data, the clustering is essential for fast retrieval. However, it is difficult to cluster huge image data pertinently, Because current retrieval methods using similarities have several problems like low accuracy of retrieving and long retrieval time, a solution is necessary to complement these problems. This paper presents a content-based image retrieval system with neural network in order to reinforce abovementioned problems. The retrieval system using fuzzy ART algorithm normalizes color and texture as feature values of input data between 0 and 1, and then it runs after clustering the input data. The implemental result with 300 image data shows retrieval accuracy of approximately 87%.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.4
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• pp.9-16
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• 2002

Content based Image retrieval methods in the multimedia information retrievals use primary visual features such as color, texture and shape. Color and texture generally are used as features of the image retrieval systems. But these systems may produce errors in similar image retrieval because two images with different shapes can represent very different contents. Therefore, the use of shape describing features is essential in an efficient content based image retrieval system. In this paper, after the global features filtering process by the boundary of objects, we have created a better shape similarity image retrieval system by a histogram of shape information.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.826-831
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• 1988

This paper presents an approach to segment an image into areas of surfaces, and to compute the surface properties from a gray-scale image in order to describe the surfaces for reconstruction of the 3-D shape of the objects. In general, an rigid body has several surfaces and many edges. But if it is not polyhedoron, it is necessary not only to describe the relation between surfaces, i.e. its line drawings but also to represent the surfaces' equations itself. In order to compute the surfaces' equation we use a probability of edge distribution. At first it is extracted edges from a gray-level image as much as possible. These are not only the points that maximize the change of an image intensuty but candidates which can be seemed to be edges. Next, other character of a surface (color, coordinates and image intensity) are extracted. In our study, we call the all feature of a surface as "texture", for example color, intensity level, orientation of an edge, shape of a surface and so on. These features of a surface on a pixel of an image plane are mapped to a point of the feature space, and segmented to each groups by cluster analysis on this space. These groups are considered to represent object surface in an image plane. Finally, the states of object surface in 3-D space are computed from distributional probability of local and overall statistical features of a surface, and from shape of a surface.a surface.