Image Holistic Scene Understanding Based on Image Intrinsic Characteristics and Conditional Random Fields
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- 발행기관
- 보안공학연구지원센터(IJSIP) 바로가기
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- 간행물
- International Journal of Signal Processing, Image Processing and Pattern Recognition 바로가기
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- 통권
- Vol.9 No.3 (2016.03)바로가기
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- 페이지
- pp.17-36
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A271021
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- Image holistic scene understanding based on image intrinsic characteristics and conditional random fields is proposed. The model integrates image scene classification, image semantic segmentation and object detection. 1) For the scene classification, we use method of PHOW feature extraction plus KPCA dimensional reduction to obtain feature information for each image. 2) For object detection section, saliency detection and segmentation characteristics of the image object detection is useful. We propose the method by integrating image segmentation information got by the method proposed in literature [1]. 3) For the semantic segmentation: (1) For the unary potentials, we incorporating HOG, RGB color histogram and LBP features by the methods proposed in literature [2]; (2) The image manifold structural features can better reflect the importance between hyper-pixel regions and eventually boost accuracy. Therefore, we add the higher-order potential item to reflect inherent manifold image feature of each super pixel region. The experiments testify that model performance has raised on all three sub-tasks.
목차
Abstract
1. Introduction
2. Related Works
3. Our Holistic Scene Understanding Framework
4. Image Intrinsic Feature Fusion
4.1. Unary Potential Feature Information
4.2. Image Manifold Feature Information
4.3. Image Holistic Class Feature Information
4.4. Image Saliency Detection Information
5. Feature Engineering of Holistic Scene Understanding
5.1. Segmentation Potential
5.2. Scene Existence Potential
5.3. Object Detection Potential
5.4. Object Detection Potential
6. Experimental Design
6.1. Datasets
6.2. Experimental Platforms
6.3. Experimental Settings
7. Experimental Result and Analysis
7.1 Image Scene Classification
7.2 Image Semantic Segmentation
7.3 Object Detection
7.4 The Impact Analysis of Mask Size
8. Conclusion and future work
Acknowledgements
References
키워드
image holistic scene understanding scene understanding conditional random fields image intrinsic characteristics probabilistic graphical model저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJSIP) [Science & Engineering Research Support Center, Republic of Korea(IJSIP)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of Signal Processing, Image Processing and Pattern Recognition
- 간기
- 격월간
- pISSN
- 2005-4254
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.9 No.3
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