Cargando…
Learning-based local visual representation and indexing /
Learning-Based Local Visual Representation and Indexing , reviews the state-of-the-art in visual content representation and indexing, introduces cutting-edge techniques in learning based visual representation, and discusses emerging topics in visual local representation, and introduces the most rece...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam ; Waltham, MA :
Elsevier,
2014.
|
| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
Tabla de Contenidos:
- Front Cover; Learning-Based Local Visual Representation and Indexing; Copyright; Contents; Preface; List of Figures; List of Tables; List of Algorithms; Chapter 1: Introduction; 1.1 Background and Significance; 1.2 Literature Review of the Visual Dictionary; 1.2.1 Local Interest-Point Extraction; 1.2.2 Visual-Dictionary Generation and Indexing ; 1.3 Contents of This Book; Chapter 2: Interest-Point Detection: Beyond Local Scale; 2.1 Introduction; 2.2 Difference of Contextual Gaussians; 2.2.1 Local Interest-Point Detection; 2.2.2 Accumulating Contextual Gaussian Difference.
- 2.3 Mean Shift-Based Localization2.3.1 Localization Algorithm ; 2.3.2 Comparison to Saliency; 2.4 Detector Learning; 2.5 Experiments; 2.5.1 Database and Evaluation Criteria; 2.5.2 Detector Repeatability; 2.5.3 CASL for Image Search and Classification; 2.6 Summary; Chapter 3: Unsupervised Dictionary Optimization; 3.1 Introduction; 3.2 Density-Based Metric Learning; 3.2.1 Feature-Space Density-Field Estimation ; 3.2.2 Learning a Metric for Quantization; 3.3 Chain-Structure Recognition ; 3.3.1 Chain Recognition in Dictionary Hierarchy; 3.4 Dictionary Transfer Learning.
- 3.4.1 Cross-database Case3.4.2 Incremental Transfer; 3.5 Experiments; 3.5.1 Quantitative results; 3.6 Summary; Chapter 4: Supervised Dictionary Learning via Semantic Embedding ; 4.1 Introduction; 4.2 Semantic Labeling Propagation; 4.2.1 Density Diversity Estimation ; 4.3 Supervised Dictionary Learning; 4.3.1 Generative Modeling ; 4.3.2 Supervised Quantization ; 4.4 Experiments; 4.4.1 Database and Evaluations; 4.4.2 Quantitative Results; 4.5 Summary; Chapter 5: Visual Pattern Mining; 5.1 Introduction; 5.2 Discriminative 3D Pattern Mining; 5.2.1 The Proposed Mining Scheme.
- 5.2.2 Sparse Pattern Coding5.3 CBoP for Low Bit Rate Mobile Visual Search; 5.4 Quantitative Results; 5.4.1 Data Collection; 5.4.2 Evaluation Criteria; 5.4.3 Baselines; 5.4.4 Quantitative Performance; 5.5 Conclusion; Conclusions; References.