The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 텍스처 분할을 위해 해당 필터 뱅크의 출력에서 추정된 2차원 웨이블릿 프레임의 극한 밀도를 특징으로 하는 새로운 기능을 제안합니다. 특징 선택 유무에 관계없이 피라미드형 분해와 트리 구조 분해를 기반으로 한 특징의 식별 능력은 각각 극한 밀도, 에너지 및 엔트로피를 특징으로 사용하여 비교 연구됩니다. 이러한 비교는 분리 가능한 웨이블릿과 분리 불가능한 웨이블릿을 통해 설명됩니다. Brodatz 앨범의 XNUMX개, XNUMX개, XNUMX개 범주의 질감 이미지를 사용하면 특징 선택이 있는 대부분의 성능이 특징 선택이 없는 성능보다 크게 향상되는 것으로 관찰됩니다. 또한, 실험 결과는 극한 밀도 기반 측정이 조사된 세 가지 유형의 특징 중에서 가장 잘 수행된다는 것을 보여줍니다. 선택된 특징의 각 하위 집합에 대한 분할 성능을 평가하기 위해 공간 분리 기준(SPC)을 평가 함수로 사용하는 새로운 접근 방식인 유전자 알고리즘을 기반으로 하는 Min-Min 방법이 제시됩니다. 이 연구에서 SPC는 클래스 내 유클리드 거리를 공간 영역의 클래스 간 유클리드 거리로 나눈 값으로 정의됩니다. 특징 선택을 통해 분리 불가능한 웨이블릿 프레임을 기반으로 한 트리 구조의 웨이블릿 분해가 분리 가능한 웨이블릿 프레임을 기반으로 한 트리 구조의 웨이블릿 분해와 분리 가능 및 비분리 가능한 웨이블릿 프레임을 기반으로 한 피라미드 분해에 비해 실험에서 더 좋은 성능을 보이는 것으로 나타났습니다. . 마지막으로 텍스처 이미지의 템플릿을 사용하여 평가된 분할 결과를 비교하고 제안된 기준의 유효성을 검증합니다. 또한, 특징의 차별적 특성은 특징 선택 벡터의 모든 서브밴드에 걸쳐 퍼져 있음이 입증되었습니다.
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부
Jeng-Shyang PAN, Jing-Wein WANG, "Texture Segmentation Using Separable and Non-Separable Wavelet Frames" in IEICE TRANSACTIONS on Fundamentals,
vol. E82-A, no. 8, pp. 1463-1474, August 1999, doi: .
Abstract: In this paper, a new feature which is characterized by the extrema density of 2-D wavelet frames estimated at the output of the corresponding filter bank is proposed for texture segmentation. With and without feature selection, the discrimination ability of features based on pyramidal and tree-structured decompositions are comparatively studied using the extrema density, energy, and entropy as features, respectively. These comparisons are demonstrated with separable and non-separable wavelets. With the three-, four-, and five-category textured images from Brodatz album, it is observed that most performances with feature selection improve significantly than those without feature selection. In addition, the experimental results show that the extrema density-based measure performs best among the three types of features investigated. A Min-Min method based on genetic algorithms, which is a novel approach with the spatial separation criterion (SPC) as the evaluation function is presented to evaluate the segmentation performance of each subset of selected features. In this work, the SPC is defined as the Euclidean distance within class divided by the Euclidean distance between classes in the spatial domain. It is shown that with feature selection the tree-structured wavelet decomposition based on non-separable wavelet frames has better performances than the tree-structured wavelet decomposition based on separable wavelet frames and pyramidal decomposition based on separable and non-separable wavelet frames in the experiments. Finally, we compare to the segmentation results evaluated with the templates of the textured images and verify the effectiveness of the proposed criterion. Moreover, it is proved that the discriminatory characteristics of features do spread over all subbands from the feature selection vector.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e82-a_8_1463/_p
부
@ARTICLE{e82-a_8_1463,
author={Jeng-Shyang PAN, Jing-Wein WANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Texture Segmentation Using Separable and Non-Separable Wavelet Frames},
year={1999},
volume={E82-A},
number={8},
pages={1463-1474},
abstract={In this paper, a new feature which is characterized by the extrema density of 2-D wavelet frames estimated at the output of the corresponding filter bank is proposed for texture segmentation. With and without feature selection, the discrimination ability of features based on pyramidal and tree-structured decompositions are comparatively studied using the extrema density, energy, and entropy as features, respectively. These comparisons are demonstrated with separable and non-separable wavelets. With the three-, four-, and five-category textured images from Brodatz album, it is observed that most performances with feature selection improve significantly than those without feature selection. In addition, the experimental results show that the extrema density-based measure performs best among the three types of features investigated. A Min-Min method based on genetic algorithms, which is a novel approach with the spatial separation criterion (SPC) as the evaluation function is presented to evaluate the segmentation performance of each subset of selected features. In this work, the SPC is defined as the Euclidean distance within class divided by the Euclidean distance between classes in the spatial domain. It is shown that with feature selection the tree-structured wavelet decomposition based on non-separable wavelet frames has better performances than the tree-structured wavelet decomposition based on separable wavelet frames and pyramidal decomposition based on separable and non-separable wavelet frames in the experiments. Finally, we compare to the segmentation results evaluated with the templates of the textured images and verify the effectiveness of the proposed criterion. Moreover, it is proved that the discriminatory characteristics of features do spread over all subbands from the feature selection vector.},
keywords={},
doi={},
ISSN={},
month={August},}
부
TY - JOUR
TI - Texture Segmentation Using Separable and Non-Separable Wavelet Frames
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1463
EP - 1474
AU - Jeng-Shyang PAN
AU - Jing-Wein WANG
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E82-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 1999
AB - In this paper, a new feature which is characterized by the extrema density of 2-D wavelet frames estimated at the output of the corresponding filter bank is proposed for texture segmentation. With and without feature selection, the discrimination ability of features based on pyramidal and tree-structured decompositions are comparatively studied using the extrema density, energy, and entropy as features, respectively. These comparisons are demonstrated with separable and non-separable wavelets. With the three-, four-, and five-category textured images from Brodatz album, it is observed that most performances with feature selection improve significantly than those without feature selection. In addition, the experimental results show that the extrema density-based measure performs best among the three types of features investigated. A Min-Min method based on genetic algorithms, which is a novel approach with the spatial separation criterion (SPC) as the evaluation function is presented to evaluate the segmentation performance of each subset of selected features. In this work, the SPC is defined as the Euclidean distance within class divided by the Euclidean distance between classes in the spatial domain. It is shown that with feature selection the tree-structured wavelet decomposition based on non-separable wavelet frames has better performances than the tree-structured wavelet decomposition based on separable wavelet frames and pyramidal decomposition based on separable and non-separable wavelet frames in the experiments. Finally, we compare to the segmentation results evaluated with the templates of the textured images and verify the effectiveness of the proposed criterion. Moreover, it is proved that the discriminatory characteristics of features do spread over all subbands from the feature selection vector.
ER -