• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.27-37
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• 2008

Applications of 3D data increase with advancement of multimedia technique and contents, and it is necessary to manage and to retrieve for 3D data efficiently. In this paper, we propose a new method using the sliced shape which extracts efficiently a feature description for shape-based retrieval of 3D models. Since the feature descriptor of 3D model should be invariant to translation, rotation and scale for its model, normalization of models requires for 3D model retrieval system. This paper uses principal component analysis(PCA) method in order to normalize all the models. The proposed algorithm finds a direction of each axis by the PCA and creates orthogonal n planes in each axis. These planes are orthogonalized with each axis, and are used to extract sliced shape image. Sliced shape image is the 2D plane created by intersecting at between 3D model and these planes. The proposed feature descriptor is a distribution of Euclidean distances from center point of sliced shape image to its outline. A performed evaluation is used for average of the normalize modified retrieval rank(ANMRR) with a standard evaluation from MPEG-7. In our experimental results, we demonstrate that the proposed method is an efficient 3D model retrieval.

• Journal of Broadcast Engineering
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• v.2 no.1
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• pp.74-83
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• 1997

We have proposed a composite feature measure which combines the color and shape features of an image for image retrieval. We improved the performance of retrieval based on the efficient color quantization using the Lloyd-Max quanizer and on the Histogram matrix matching method which considers the spatial correlation of quantized color group. We also supplemented the color information using shape information with the Improved Moment Invarlants. We have tested our technique on Image database consisting of 200 actual trademark images. Our experimental results showed that our approach improved the performance compared to the previous method under the various situations such as rotation images, translation images, noise added images, gamma corrected images and so on. The efficiency of retrieval is found to be very high and experimental results are

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.7
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• pp.453-457
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• 2005

With the expansion of Internet, a variety of image databases are widely used and it is needed to select the part of an image what he wants. In contents-based image retrieval system, Zernikie moment and ART Descriptors are used fur shape descriptors in MPEC-7. This paper presents a fast computation method to determine the radius of a bounding circle that encloses an object in a binary image. With conventional methods, the whole area of the image should be scanned first and the distance from every pixel to the center point be computed. The proposed 4-directional scan method and fast circle-drawing algorithm is utilized to minimize the scanning area and reduce the number of operations fur computing the distance. Experimental results show that proposed method saves the computation time to determine the radius of a bounding circle efficiently.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.375-378
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• 2000

In this paper, we propose a novo] content-based image retrieval system using both Hidden Markov Model(HMM) and an improved chain code. The Gaussian Mixture Model(GMM) is applied to statistically model a color information of the image, and Deterministic Annealing EM(DAEM) algorithm is employed to estimate the parameters of GMM. This result is used to segment the given image. We use an improved chain code, which is invariant to rotation, translation and scale, to extract the feature vectors of the shape for each image in the database. These are stored together in the database with each HMM whose parameters (A, B, $\pi$) are estimated by Baum-Welch algorithm. With respect to feature vector obtained in the same way from the query image, a occurring probability of each image is computed by using the forward algorithm of HMM. We use these probabilities for the image retrieval and present the highest similarity images based on these probabilities.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.57-63
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• 2009

The rapid growth of computer-based image database, necessity of a system that can manage an image information is increasing. This paper presents a region-based image retrieval method using the combination of color(autocorrelogram), texture(CWT moments) and shape(Hu invariant moments) features. As a color feature, a color autocorrelogram is chosen by extracting from the hue and saturation components of a color image(HSV). As a texture, shape and position feature are extracted from the value component. For efficient similarity confutation, the extracted features(color autocorrelogram, Hu invariant moments, and CWT moments) are combined and then precision and recall are measured. Experiment results for Corel and VisTex DBs show that the proposed image retrieval algorithm has 94.8% Precision, 90.7% recall and can successfully apply to image retrieval system.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.1
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• pp.33-38
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• 2001

In this paper, we present an image retrieval system using shape features. The preprocessing to gain shape feature includes edge extraction using chain code. The shape features consist of center of mass, standard deviation, ratio of major axis and minor axis length. The similarity is estimated as comparing the features of query image with the features of images in database. Thus, the candidates of images are retrieved according to the order of similarity. The result of an experimentation is dullness for scale, rotation and translation. We evaluate the performance of shape features for image retrieval on a database with over 170 images. The Recall and the Precision is each 0.72 and 0.83 in the result of average experiment. So the proposed method is presented useful method.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.2
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• pp.9-15
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• 2000

In this paper, we proposed a content-based image retrieval method using color and object's complexity for indexing of image database. Generally, the retrieval methods using color feature can not sufficiently include the spatial information in the image. So they are reduced retrieval efficiency. Then we combined object's complexity which extracted from chain code and the conventional color feature. As a result, experiments shooed that the proposed method which considers the shape feature improved performance in conducting content-based search.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.4
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• pp.333-337
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• 2002

A content-based image retrieval method using color, texture, and shape features is proposed in this paper. A region segmentation technique using PIM(Picture Information Measure) entropy is used for similarity indexing. For segmentation, a color image is first transformed to a gray image and it is divided into n$\times$n non-overlapping blocks. Entropy using PIM is obtained from each block. Adequate variance to perform good segmentation of images in the database is obtained heuristically. As variance increases up to some bound, objects within the image can be easily segmented from the background. Therefore, variance is a good indication for adequate image segmentation. For high variance image, the image is segmented into two regions-high and low entropy regions. In high entropy region, hue-saturation-intensity and canny edge histograms are used for image similarity calculation. For image having lower variance is well represented by global texture information. Experiments show that the proposed method displayed similar images at the average of 4th rank for top-10 retrieval case.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.263-272
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• 2007

This paper proposes a skeleton-based hierarchical shape description scheme, called a skeleton tree, for accurate shape-based image retrieval. A skeleton tree represents an object shape as a hierarchical tree where high-level nodes describe parts of coarse trunk regions and low-level nodes describe fine details of boundary regions. Each node refines the shape of its parent node. Most of the noise disturbances are limited to bottom level nodes and the boundary noise is reduced by decreasing weights on the bottom levels. The similarity of two skeleton trees is computed by considering the best match of a skeleton tree to a sub-tree of another skeleton tree. The proposed method uses a hybrid similarity measure by employing both Fourier descriptors and moment invariants in computing the similarity of two skeleton trees. Several experimental results are presented demonstrating the validity of the skeleton tree scheme for the shape description and indexing.

• Science of Emotion and Sensibility
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• v.7 no.4
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• pp.57-65
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• 2004

In this paper, we present a hybrid approach which incorporates color, texture and shape in content-based image retrieval. Colors in each image are clustered into a small number of representative colors. The feature descriptor consists of the representative colors and their percentages in the image. A similarity measure similar to the cumulative color histogram distance measure is defined for this descriptor. The co-occurrence matrix as a statistical method is used for texture analysis. An optimal set of five statistical functions are extracted from the co-occurrence matrix of each image, in order to render the feature vector for eachimage maximally informative. The edge information captured within edge histograms is extracted after a pre-processing phase that performs color transformation, quantization, and filtering. The features where thus extracted and stored within feature vectors and were later compared with an intersection-based method. The content-based retrieval system is tested to be effective in terms of retrieval and scalability through experimental results and precision-recall analysis.