Image Holistic Scene Understanding Based on Global Contextual Features and Bayesian Topic Model
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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.2 (2016.02)바로가기
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- 페이지
- pp.247-270
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A270131
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- Image holistic scene understanding based on global contextual features and Bayesian topic model is proposed. The model integrates three basic subtasks: the scene classification, image annotation and semantic segmentation. The model takes full advantage of global feature information in two aspects. On the one side, the performance of image scene classification and image annotation are boosted by incorporating image global contextual features; On the other side, the performance of image semantic segmentation is also boosted by new superpixel region segmentation method and new superpixel regions and patch feature representation. 1) For image scene classification and image annotation: (1) We improve the feature engineering methods by using the PHOW proposed by Vedaldi [1]; (2) Furthermore, global contextual features are learned by semantic features. 2) For semantic segmentation: (1) We improve the super-pixel segmentation method by using UCM in the literature [2]; (2)We proposed new feature representation for super-pixel region and patches by incorporating DSIFT, texton filter banks, RGB color, HOG, LBP and location features. The experiments testify that model performance has raised on all three sub-tasks.
목차
Abstract
1. Introduction
2. Related Work
3. Framework of Image Holistic Scene Understanding Based on Global Contextual Features and Bayesian Topic Model
3.1. The Generative Model
3.2. Generating the Visual Component
3.3. Generating the Tag Component
4. Composite Model
5. Feature Engineering of Global Contextual Model
5.1. Semantic Feature Extraction
5.2. Global Contextual Feature
5.3. New Feature Representation for Superpixel Regions and Patches
6. Model Learning
7. Model Inference
7.1. Image Classification
7.2. Image Annotation
7.3. Image Segmentation
8. Experimental Design
8.1. Data Sets
8.2. Experimental setup
9. Experimental Results and Analysis
9.1. Image Scene Classification
9.2. Image annotation
9.3. Image Segmentation
9.4. Experimental Discussion
10. Conclusions and Future Works
References
키워드
Image holistic scene understanding Scene understanding Global contextual features Bayesian topic model Probabilistic graphical models저자
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.2
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