• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.288-301
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• 2017

In this paper, we propose an optimized algorithm for texture classification by computing a completed modeling of the local binary pattern (CLBP) instead of the traditional LBP of a scalable block size in an image. First, we show that the CLBP descriptor is a better representative than LBP by extracting more information from an image. Second, the CLBP features of scalable block size of an image has an adaptive capability in representing both gross and detailed features of an image and thus it is suitable for image texture classification. This paper successfully implements a machine learning scheme by applying the CLBP features of a scalable size to the Support Vector Machine (SVM) classifier. The proposed scheme has been evaluated on Outex and CUReT databases, and the evaluation result shows that the proposed approach achieves an improved recognition rate compared to the previous research results.

• Science of Emotion and Sensibility
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• v.5 no.3
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• pp.11-20
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• 2002

There are Two new models developed for objective evaluation of fabric color patterns by applying a multiple regression analysis and an adaptive foray-rule-based system. The physical features of fabric color patterns are extracted through digital image processing and the emotional features are collected based on the psychological experiments of Soen[3, 4]. The principle physical features are hue, saturation, intensity and the texture of color patterns. The emotional features arc represented thirteen pairs of adverse adjectives. The multiple regression analyses and the adaptive fuzzy system are used as a tool to analyze the relations between physical and emotional features. As a result, both of the proposed models show competent performance for the approximation and the similar linguistic interpretation to the Soen's psychological experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.1
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• pp.211-227
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• 2011

Content Based Image Retrieval (CBIR) is an approach for retrieving similar images from an image database based on automatically-derived image features. The quality of a retrieval system depends on the features used to describe image content. In this paper, we propose an image clustering system that takes a database of images as input and clusters them using k-means clustering algorithm taking into consideration color, texture and shape features. Experimental results show that the combination of the three features brings about higher values of accuracy and precision.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2400-2419
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• 2014

Pre-segmented pixels can reduce the difficulty of segmentation and promote the segmentation performance. This paper proposes a novel segmentation method based on merging texture superpixels by computing inner similarity. Firstly, we design a set of Gabor filters to compute the amplitude responses of original image and compute the texture map by a salience model. Secondly, we employ the simple clustering to extract superpixles by affinity of color, coordinates and texture map. Then, we design a normalized histograms descriptor for superpixels integrated color and texture information of inner pixels. To obtain the final segmentation result, all adjacent superpixels are merged by the homogeneity comparison of normalized color-texture features until the stop criteria is satisfied. The experiments are conducted on natural scene images and synthesis texture images demonstrate that the proposed segmentation algorithm can achieve ideal segmentation on complex texture regions.

• The Transactions of the Korea Information Processing Society
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• v.6 no.1
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• pp.253-260
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• 1999

In this paper, we suggest a new direct method for mage segmentation using texture and color information combined through a multivariate linear discriminant algorithm. The color texture is computed in nin 3${\times}$3 masks obtained from each 3${\times}$3${\times}$3 spatio-spectral neighborhood in the image using the classical haralick and Pressman texture features. Among these 9${\times}$28 texture features the best set was extracted from a training set. The resulting set of 10 features were used to segment an image into four different regions. The resulting segmentation was Compared to classical color and texture segmentation methods using both box classifiers and maximum likelihood classification. It compared favourably on the test image from a Fastred-Lightgreen stained prostatic histological tissue section based on visual inspection. The classification accuracy of 97.5% for the new method obtained on the training data was also among the best of the tested methods. If these results hold for a larger set of images, this method should be a useful tool for segmenting images where both color and texture are relevant for the segmentation process.

• Journal of the Korean Society for Heat Treatment
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• v.32 no.6
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• pp.256-262
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• 2019

The microstructural features and texture development by both hot rolling and hot forging in ${\gamma}-TiAl$ alloy were investigated. In addition, additional heat treatment after hot forging was conducted to recognize change of the microstructure and texture evolution. The obtained microstructural features through dynamic recrystallization after hot deformed ${\gamma}-TiAl$ were quite different because two kinds of formation process were occurred depending on deformation condition. However, analyzed texture tends to be random orientation due to intermediate annealing up to ${\alpha}+{\beta}$ region during the hot deformation process. After additional heat treatment, microstructure transformed into fully lamellar microstructure and randomly oriented texture was also observed due to the same reason as before. Tensile test at room temperature demonstrated that anisotropy of mechanical properties were not appeared and transgranular fracture was occurred between interface of ${\alpha}_2/{\gamma}$. As a result, it could be suggested that microstructural features influenced much more than texture development on mechanical properties at room temperature.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.176-188
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• 2022

In this paper, we propose a compact method for anthropomorphism that uses Deep Convolutional Neural Networks (DCNN) to detect the similarities between a human face and an animal face. We also apply texture feature-based morphing between them. We propose a basic texture feature-based morphing system for morphing between human faces only. The entire anthropomorphism process starts with the creation of an animal face classifier using a parallel DCNN that determines the most similar animal face to a given human face. The significance of our network is that it contains four sets of convolutional functions that run in parallel, allowing it to extract more features than a linear DCNN network. Our employed texture feature algorithm-based automatic morphing system recognizes the facial features of the human face and takes the Control Points automatically, rather than the traditional human aiding manual morphing system, once the similarity was established. The simulation results show that our suggested DCNN surpasses its competitors with a 92.0% accuracy rate. It also ensures that the most similar animal classes are found, and the texture-based morphing technology automatically completes the morphing process, ensuring a smooth transition from one image to another.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.178-181
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• 2005

Support vector machines is a powerful learning algorithm and attempt to separate belonging to two given sets in N-dimensional real space by a nonlinear surface, often only implicitly dened by a kernel function. We described breast tissue images analyses using texture features from Haar wavelet transformed images to classify breast lesion of ductal organ Benign, DCIS and CA. The approach for creating a classifier is composed of 2 steps: feature extraction and classification. Therefore, in the feature extraction step, we extracted texture features from wavelet transformed images with $10{\times}$ magnification. In the classification step, we created four classifiers from each image of extracted features using SVM(Support Vector Machines). In this study, we conclude that the best classifier in histological sections of breast tissue in the texture features from second-level wavelet transformed images used in Polynomial function.

• Journal of Korea Multimedia Society
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• v.5 no.5
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• pp.505-514
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• 2002

In this Paper, we propose a method of content-based image classification using neural network. The images for classification ate object images that can be divided into foreground and background. To deal with the object images efficiently, object region is extracted with a region segmentation technique in the preprocessing step. Features for the classification are texture and shape features extracted from wavelet transformed image. The neural network classifier is constructed with the extracted features and the back-propagation learning algorithm. Among the various texture features, the diagonal moment was more effective. A test with 300 training data and 300 test data composed of 10 images from each of 30 classes shows correct classification rates of 72.3% and 67%, respectively.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.129-137
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• 1994

In this paper, a new texture feature extraction method for texture image classification is proposed. The proposed method is a modified texture spectrum method. It uses local weighting averaged texture unit, that is, the neighbor pixels are weithted and averaged in 4-direction and the calculated values are compared with center pixel to find texture units. The proposed method has only 81 texture units and these units are really good features for texture classification. The proposed method is applied to vegetable images and Blodatz album images and compared with several conventional methods for the feature extraction time and the recognition rate.