• Title/Summary/Keyword: Texture descriptor

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MPEG-7 Homogeneous Texture Descriptor

  • Ro, Yong-Man;Kim, Mun-Churl;Kang, Ho-Kyung;Manjunath, B.S.;Kim, Jin-Woong
    • ETRI Journal
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    • v.23 no.2
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    • pp.41-51
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    • 2001
  • MPEG-7 standardization work has started with the aims of providing fundamental tools for describing multimedia contents. MPEG-7 defines the syntax and semantics of descriptors and description schemes so that they may be used as fundamental tools for multimedia content description. In this paper, we introduce a texture based image description and retrieval method, which is adopted as the homogeneous texture descriptor in the visual part of the MPEG-7 final committee draft. The current MPEG-7 homogeneous texture descriptor consists of the mean, the standard deviation value of an image, energy, and energy deviation values of Fourier transform of the image. These are extracted from partitioned frequency channels based on the human visual system (HVS). For reliable extraction of the texture descriptor, Radon transformation is employed. This is suitable for HVS behavior. We also introduce various matching methods; for example, intensity-invariant, rotation-invariant and/or scale-invariant matching. This technique retrieves relevant texture images when the user gives a querying texture image. In order to show the promising performance of the texture descriptor, we take the experimental results with the MPEG-7 test sets. Experimental results show that the MPEG-7 texture descriptor gives an efficient and effective retrieval rate. Furthermore, it gives fast feature extraction time for constructing the texture descriptor.

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Iris Recognition using MPEG-7 Homogeneous Texture Descriptor (MPEG-7 Homogeneous Texture 기술자를 이용한 홍채인식)

  • 이종민;한일호;김희율
    • Proceedings of the IEEK Conference
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    • pp.45-48
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    • 2002
  • In this paper, we propose an iris recognition system using Homogeneous Texture descriptor of MPEG-7 standard. The texture of iris is generally used in iris recognition system. We segment the pupil with Hough transform and the boundary of iris with it's gray level difference between the white of the eye. To extract Homogeneous Texture descriptor, this iris image is transformed into polar coordinates. The extracted descriptor is then compared with the reference in DB. If their distance is larger than threshold, they are recognized as different iris. Test results will show that Homogeneous Texture descriptor can be a good measure for iris recognition system.

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MPEG-7 Texture Descriptor (MPEG-7 질감 기술자)

  • 강호경;정용주;유기원;노용만;김문철;김진웅
    • 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.

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Texture Classification Using Local Neighbor Differences (지역 근처 차이를 이용한 텍스쳐 분류에 관한 연구)

  • Saipullah, Khairul Muzzammil;Peng, Shao-Hu;Park, Min-Wook;Kim, Deok-Hwan
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.377-380
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    • 2010
  • This paper proposes texture descriptor for texture classification called Local Neighbor Differences (LND). LND is a high discriminating texture descriptor and also robust to illumination changes. The proposed descriptor utilizes the sign of differences between surrounding pixels in a local neighborhood. The differences of those pixels are thresholded to form an 8-bit binary codeword. The decimal values of these 8-bit code words are computed and they are called LND values. A histogram of the resulting LND values is created and used as feature to describe the texture information of an image. Experimental results, with respect to texture classification accuracies using OUTEX_TC_00001 test suite has been performed. The results show that LND outperforms LBP method, with average classification accuracies of 92.3% whereas that of local binary patterns (LBP) is 90.7%.

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Texture Descriptor for Texture-Based Image Retrieval and Its Application in Computer-Aided Diagnosis System (질감 기반 이미지 검색을 위한 질감 서술자 및 컴퓨터 조력 진단 시스템의 적용)

  • Saipullah, Khairul Muzzammil;Peng, Shao-Hu;Kim, Deok-Hwan
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.47 no.4
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    • pp.34-43
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    • 2010
  • Texture information plays an important role in object recognition and classification. To perform an accurate classification, the texture feature used in the classification must be highly discriminative. This paper presents a novel texture descriptor for texture-based image retrieval and its application in Computer-Aided Diagnosis (CAD) system for Emphysema classification. The texture descriptor is based on the combination of local surrounding neighborhood difference and centralized neighborhood difference and is named as Combined Neighborhood Difference (CND). The local differences of surrounding neighborhood difference and centralized neighborhood difference between pixels are compared and converted into binary codewords. Then binomial factor is assigned to the codewords in order to convert them into high discriminative unique values. The distribution of these unique values is computed and used as the texture feature vectors. The texture classification accuracies using Outex and Brodatz dataset show that CND achieves an average of 92.5%, whereas LBP, LND and Gabor filter achieve 89.3%, 90.7% and 83.6%, respectively. The implementations of CND in the computer-aided diagnosis of Emphysema is also presented in this paper.

A Study on Image Retrieval Method Using Texture Descriptor (질감 기술자를 이용한 영상 검색 기법에 관한 연구)

  • Cho, Jae-Hoon;Chong, Hyun-Jin;Kim, Young-Seop
    • Proceedings of the IEEK Conference
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    • pp.745-746
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    • 2008
  • In the last few years rapid improvements in hardware technology have made it possible to process, store and retrieve huge amounts of data ina multimedia format. As a result, Content-Based Image Retrieval(CBIR) has been receiving widespred interest during the last decade. This paper propose the content-based retrieval system as a method for performing image retrieval throught the effective feature analysis of the object of significant meaning by using texture descriptor.

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Texture Descriptor Using Correlation of Quantized Pixel Values on Intensity Range (화소값의 구간별 양자화 값 상관관계를 이용한 텍스춰 기술자)

  • Pok, Gouchol
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.11 no.3
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    • pp.229-234
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    • 2018
  • Texture is one of the most useful features in classifying and segmenting images. The LBP-based approach previously presented in the literature has been successful in many applications. However, it's theoretical foundation is based only on the difference of pixel values, and consequently it has a number of drawbacks like it performs poorly for the images corrupted with noise, and especially it cannot be used as a multiscale texture descriptor due to the exploding increase of feature vector dimension with increase of the number of neighbor pixels. In this paper, we present a method to address these drawbacks of LBP-based approach. More specifically, our approach quantizes the range of pixels values and construct a 3D histogram which captures the correlative information of pixels. This histogram is used as a texture feature. Several tests with texture images show that the proposed method outperforms the LBP-based approach in the problem of texture classification.

An approach for improving the performance of the Content-Based Image Retrieval (CBIR)

  • Jeong, Inseong
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.30 no.6_2
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    • pp.665-672
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    • 2012
  • Amid rapidly increasing imagery inputs and their volume in a remote sensing imagery database, Content-Based Image Retrieval (CBIR) is an effective tool to search for an image feature or image content of interest a user wants to retrieve. It seeks to capture salient features from a 'query' image, and then to locate other instances of image region having similar features elsewhere in the image database. For a CBIR approach that uses texture as a primary feature primitive, designing a texture descriptor to better represent image contents is a key to improve CBIR results. For this purpose, an extended feature vector combining the Gabor filter and co-occurrence histogram method is suggested and evaluated for quantitywise and qualitywise retrieval performance criterion. For the better CBIR performance, assessing similarity between high dimensional feature vectors is also a challenging issue. Therefore a number of distance metrics (i.e. L1 and L2 norm) is tried to measure closeness between two feature vectors, and its impact on retrieval result is analyzed. In this paper, experimental results are presented with several CBIR samples. The current results show that 1) the overall retrieval quantity and quality is improved by combining two types of feature vectors, 2) some feature is better retrieved by a specific feature vector, and 3) retrieval result quality (i.e. ranking of retrieved image tiles) is sensitive to an adopted similarity metric when the extended feature vector is employed.

Plants Disease Phenotyping using Quinary Patterns as Texture Descriptor

  • Ahmad, Wakeel;Shah, S.M. Adnan;Irtaza, Aun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.8
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    • pp.3312-3327
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    • 2020
  • Plant diseases are a significant yield and quality constraint for farmers around the world due to their severe impact on agricultural productivity. Such losses can have a substantial impact on the economy which causes a reduction in farmer's income and higher prices for consumers. Further, it may also result in a severe shortage of food ensuing violent hunger and starvation, especially, in less-developed countries where access to disease prevention methods is limited. This research presents an investigation of Directional Local Quinary Patterns (DLQP) as a feature descriptor for plants leaf disease detection and Support Vector Machine (SVM) as a classifier. The DLQP as a feature descriptor is specifically the first time being used for disease detection in horticulture. DLQP provides directional edge information attending the reference pixel with its neighboring pixel value by involving computation of their grey-level difference based on quinary value (-2, -1, 0, 1, 2) in 0°, 45°, 90°, and 135° directions of selected window of plant leaf image. To assess the robustness of DLQP as a texture descriptor we used a research-oriented Plant Village dataset of Tomato plant (3,900 leaf images) comprising of 6 diseased classes, Potato plant (1,526 leaf images) and Apple plant (2,600 leaf images) comprising of 3 diseased classes. The accuracies of 95.6%, 96.2% and 97.8% for the above-mentioned crops, respectively, were achieved which are higher in comparison with classification on the same dataset using other standard feature descriptors like Local Binary Pattern (LBP) and Local Ternary Patterns (LTP). Further, the effectiveness of the proposed method is proven by comparing it with existing algorithms for plant disease phenotyping.