• Journal of Korea Multimedia Society
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• v.9 no.3
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• pp.287-295
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• 2006

This paper compares two different approaches based on wavelet and Gabor decomposition towards representing the texture of an arbitrary region. The Gabor-domain mean and standard deviation combination is considered to be best in representing the texture of rectangular regions. However, texture representation of arbitrary regions would enable generalized object-based image retrieval and other applications in the future. In this study, we have found that the wavelet features perform better than the Gabor features in representing the texture of arbitrary regions. Particularly, the wavelet-domain standard deviation and entropy combination results in the best retrieval accuracy. Based on our experiment with texture image sets, we present and compare tile retrieval accuracy of multiple wavelet and Gabor feature combinations.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.8
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• pp.778-785
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• 2009

This paper presents a Gabor texture feature extraction method for classification of discolored Metal pad images using GPU(Graphics Processing Unit). The proposed algorithm extracts the texture information using Gabor filters and constructs a pattern map using the extracted information. Finally, the golden pad images are classified by utilizing the feature vectors which are extracted from the constructed pattern map. In order to evaluate the performance of the Gabor texture feature extraction algorithm based on GPU, a sequential processing and parallel processing using OpenMP in CPU of this algorithm were adopted. Also, the proposed algorithm was implemented by using Global memory and Shared memory in GPU. The experimental results were demonstrated that the method using Shared memory in GPU provides the best performance. For evaluating the effectiveness of extracted Gabor texture features, an experimental validation has been conducted on a database of 20 Metal pad images and the experiment has shown no mis-classification.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.2
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• pp.147-153
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• 2017

Efficient texture representation is very important in computer vision fields. The performance of texture classification or/and segmentation can be improved based on efficient texture representation. Gabor filter is a representation method that has long history for texture representation based on multi-scale analysis. Gabor filter shows good performance in texture classification and segmentation but requires much processing time. In this paper, we propose new texture representation method that is also based on multi-scale analysis. The proposed representation can provide similar performance in texture classification but can reduce processing time against Gabor filter. Experimental results show good performance of our method.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.181-184
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• 2000

In this paper, we propose new features for describing texture images by using multi-channel Gabor filter and log-polar transform based on human visual system (HVS). Gabor features are extracted by the mean and standard deviation of energy in Gabor response, followed by Fourier series extension. Log-polar features are extracted by log-polar transform and projection. The proposed texture descriptor performs reasonably well with less number of features than other texture descriptors, which has been verified by experiments using some texture images of MPEG-7 data set.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.311-320
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• 2003

The Optimal filter yielding optimal texture feature separation is a most effective technique for extracting the texture objects from multiple textures images. But, most optimal filter design approaches are restricted to the issue of supervised problems. No full-unsupervised method is based on the recognition of texture objects in image. We propose a novel approach that uses unsupervised learning schemes for efficient texture image analysis, and the band-pass feature of Gabor-filter is used for the optimal filter design. In our approach, the self-organizing neural network for multiple texture image identification is based on block-based clustering. The optimal frequency of Gabor-filter is turned to the optimal frequency of the distinct texture in frequency domain by analyzing the spatial frequency. In order to show the performance of the designed filters, after we have attempted to build a various texture images. The texture objects extraction is achieved by using the designed Gabor-filter. Our experimental results show that the performance of the system is very successful.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.3
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• pp.37-44
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• 2019

In this study, we propose the automatic extraction method of Rescue Requests from Drone Images. A central object is extracted from each image by using central object extraction method[7] before classification. A central object in an images are defined as a set of regions that is lined around center of the image and has significant texture distribution against its surrounding. In this case of artificial objects, edge of straight line is often found, and texture is regular and directive. However, natural object's case is not. Such characteristics are extracted using Edge direction histogram energy and texture Gabor energy. The Edge direction histogram energy calculated based on the direction of only non-circular edges. The texture Gabor energy is calculated based on the 24-dimension Gebor filter bank. Maximum and minimum energy along direction in Gabor filter dictionary is selected. Finally, the extracted rescue requestor object areas using the dominant features of the objects. Through experiments, we obtain accuracy of more than 75% for extraction method using each features.

• Journal of Korea Multimedia Society
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• v.10 no.9
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• pp.1125-1134
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• 2007

In this paper, we propose a new approach for rotation invariant texture classification based on Gabor wavelet. Conventional methods have the low correct classification rate in large texture database. In our proposed method, we define two feature groups which are the global feature vector and the local feature matrix. The feature groups are output of Gabor wavelet filtering. By using the feature groups, we defined an improved discriminant and obtained high classification rates of large texture database in the experiments. From spectrum symmetry of texture images, the number of test times were reduced nearly 50%. Consequently, the correct classification rate is improved with $2.3%{\sim}15.6%$ values in 112 Brodatz texture class, which may vary according to comparison methods.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.7
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• pp.323-326
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• 2002

As multimedia database and digital image libraries are enlarged, CBIR(Content Based Image Retrieval) has been getting importance for the efficient search. Generally, CBIR uses primitive features such as color, shape, texture and so on. Among various methods of CBIR, Gabor wavelet has good image retrieval performance with texture features but it has a disadvantage which does not perform well for a rotated image because of its direction oriented filter. In this paper, we propose a new method to solve this problem by modifying Gabor filter for all directions. And then we will compare the searching performance of the proposed method with those of conventional image retrieval methods through experiments with trademarks.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.76-85
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• 2011

In this paper, we propose a texture feature-based language identification using Gabor feature and wavelet-domain BDIP (block difference of inverse probabilities) and BVLC (block variance of local correlation coefficients) features. In the proposed method, Gabor and wavelet transforms are first applied to a test image. The wavelet subbands are next denoised by Donoho's soft-thresholding. The magnitude operator is then applied to the Gabor image and the BDIP and BVLC operators to the wavelet subbands. Moments for Gabor magnitude image and each subband of BDIP and BVLC are computed and 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. Experimental results show that the proposed method yields excellent language identification with rather low feature dimension for a document image DB.

• The Journal of the Acoustical Society of Korea
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• v.27 no.3E
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• pp.77-83
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• 2008

In the process of the sonar image textures produced, the orientation and scale factors are very significant. However, most of the related methods ignore the directional information and scale invariance or just pay attention to one of them. To overcome this problem, we apply Gabor wavelet to extract the features of sonar images, which combine the advantages of both the Gabor filter and traditional wavelet function. The mother wavelet is designed with constrained parameters and the optimal parameters will be selected at each orientation, with the help of bandwidth parameters based on the Fisher criterion. The Gabor wavelet can have the properties of both multi-scale and multi-orientation. Based on our experiment, this method is more appropriate than traditional wavelet or single Gabor filter as it provides the better discrimination of the textures and improves the recognition rate effectively. Meanwhile, comparing with other fusion methods, it can reduce the complexity and improve the calculation efficiency.