• Journal of Biomedical Engineering Research
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• v.17 no.4
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• pp.433-442
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• 1996

Since one texture property(i.e coarseness, orientation, regularity, granularity) for ultrasound liver ages was not sufficient enough to classify the characteristics of livers, we used multi texture vectors tracted from ultrasound liver images and a statistical classifier. Multi texture vectors are selected among the feature vectors of the normal liver, fat liver and cirrhosis images which have a good separability in those ultrasound liver images. The statistical classifier uses multi texture vectors as input vectors and classifies ultrasound liver images for each multi texture vector by the Bayes decision rule. Then the decision of the liver disease is made by choosing the maximum value from the averages of a posteriori probability for each multi texture vector In our simulation, we obtained higtler correct ratio than that of other methods using single feature vector, for the test set the correct ratio is 94% in the normal liver, 84% in the fat liver and 86% in the cirrhosis liver.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.79-86
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• 2006

Classification technology is essential for fast retrieval in large multi-media database. This paper proposes a combining GA(Genetic Algorithm) and SVM(Support Vector Machine) model to fast retrieval. We used color and texture as feature vectors. We improved the retrieval accuracy by using proposed model which retrieves an optimal feature vector set in extracted feature vector sets. The first performance test was executed for the performance of color, texture and the feature vector combined with color and texture. The second performance test, was executed for performance of SVM and proposed algorithm. The results of the experiment, using the feature vector combined color and texture showed a good Performance than a single feature vector and the proposed algorithm using hybrid method also showed a good performance than SVM algorithm.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.208-214
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• 2021

Recently, much of the intelligent security scenario and criminal investigation demands for matching photo and non-photo. Existing face recognition system can not sufficiently guarantee these needs. In this paper, we propose an algorithm to improve the performance of heterogeneous face recognition systems by reducing the different modality between sketches and photos of the same person. The proposed algorithm extracts each image's texture features through texture descriptors (gray level co-occurrence matrix, multiscale local binary pattern), and based on this, generates a transformation matrix through eigenfeature regularization and extraction techniques. The score value calculated between the vectors generated in this way finally recognizes the identity of the sketch image through the score normalization methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.6
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• pp.581-590
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• 2014

With rapid land development, land category should be updated on a regular basis. However, manual field surveys have certain limitations. In this study, attempts were made to extract a feature vector considering spectral signature by parcel, PIMP (Percent Imperviousness), texture, and VIs (Vegetation Indices) based on RapidEye satellite image and cadastral map. A total of nine land categories in which feature vectors were significantly extracted from the images were selected and classified using SVM (Support Vector Machine). According to accuracy assessment, by comparing the cadastral map and classification result, the overall accuracy was 0.74. In the paddy-field category, in particular, PO acc. (producer's accuracy) and US acc. (user's accuracy) were highest at 0.85 and 0.86, respectively.

• The Journal of the Korea Contents Association
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• v.4 no.1
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• pp.18-23
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• 2004

In this paper, we propose a new visual feature extraction method for content-based image retrieval(CBIR) based on wavelet transform which has both spatial-frequency characteristic and multi-resolution characteristic. We extract visual features for each frequency band in wavelet transformation and use them to CBIR. The lowest frequency band involves spacial information of original image. We extract L feature vectors using fuzzy homogeneity in the wavelet domain, which consider both the wavelet coefficients and the spacial information of each coefficient. Also, we extract 3 feature vectors wing the energy values of high frequency bands, and store those to image database. As a query, we retrieve the most similar image from image database according to the 10 largest homograms(normalized fuzzy homogeneity vectors) and 3 energy values. Simulation results show that the proposed method has good accuracy in image retrieval using 90 texture images.

• Journal of Korea Multimedia Society
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• v.17 no.3
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• pp.277-286
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• 2014

In this paper, we propose a texture feature-based language identification by fusion of Gabor, MDLC (multi-lag directional local correlation), and co-occurrence features. In the proposed method, for a test image, Gabor magnitude images are first obtained by Gabor transform followed by magnitude operator. Moments for the Gabor magniude images are then computed and vectorized. MDLC images are then obtained by MDLC operator and their moments are computed and vectorized. GLCM (gray-level co-occurrence matrix) is next calculated from the test image and co-occurrence features are computed using the GLCM, and the features are also vectorized. The three vectors of the Gabor, MDLC, and co-occurrence features are fused into a feature vector. In classification, the WPCA (whitened principal component analysis) classifier, which is usually adopted in the face identification, searches the training feature vector most similar to the test feature vector. We evaluate the performance of our method by examining averaged identification rates for a test document image DB obtained by scanning of documents with 15 languages. Experimental results show that the proposed method yields excellent language identification with rather low feature dimension for the test DB.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.3
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• pp.184-193
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• 2010

This paper proposes a biological iridescence BRDF(Bidirectional Reflectance Distribution Function) compression and rendering method. In the graphics technology, iridescence sometimes is named structure colors. The main features of these symptoms are shown transform of color and brightness by varying viewpoint. Graphics technology to render this is the BRDF technology. The BRDF methods enable realistic representation of varying view direction, but it requires a lot of computing power because of large data. In this paper, we obtain reflection map from iridescence BRDF, analyze color of reflection map and propose representation method by several colorfully concentric circle. The one concentric circle represents beam width of reflection ray by one normal vector. In this paper, we synthesize rough concentric by using several virtually optical normal vectors. And we obtain spectrum information from concentric circles passing through the center point. The proposed method enables IBR(image based rendering) technique which results is realistic illuminance and spectrum distribution by one texture from reduced BRDF data within spectrum.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.49-59
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• 2009

Contents-based image retrieval methods are in general more objective and effective than text-based image retrieval algorithms since they use color and texture in search and avoid annotating all images for search. SIM(Self-organizing Image browsing Map) is one of contents-based image retrieval algorithms that uses only browsable mapping results obtained by SOM(Self Organizing Map). However, SOM may have an error in selecting the right BMU in learning phase if there are similar nodes with distorted color information due to the intensity of light or objects' movements in the image. Such images may be mapped into other grouping nodes thus the search rate could be decreased by this effect. In this paper, we propose an improved SIM that uses HSV color model in extracting image features with color quantization. In order to avoid unexpected learning error mentioned above, our SOM consists of two layers. In learning phase, SOM layer 1 has the color feature vectors as input. After learning SOM Layer 1, the connection weights of this layer become the input of SOM Layer 2 and re-learning occurs. With this multi-layered SOM learning, we can avoid mapping errors among similar nodes of different color information. In search, we put the query image vector into SOM layer 2 and select nodes of SOM layer 1 that connects with chosen BMU of SOM layer 2. In experiment, we verified that the proposed SIM was better than the original SIM and avoid mapping error effectively.