Semantic and Interactive Content-based Image Retrieval

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Barz, Björn
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Göttingen : Cuvillier Verlag, 2020.
Temas:
Content-based image retrieval.
Recherche d'images par le contenu.
Content-based image retrieval
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • 1 Introduction
  • 1.1 Content-based image retrieval
  • 1.2 Instance vs. category retrieval
  • 1.3 Challenges
  • 1.4 Interactive image retrieval
  • 1.5 Semantic image retrieval
  • 1.6 Contributions of this thesis
  • 2 Methodical Background
  • 2.1 Fundamental concepts and definitions
  • 2.2 Classification
  • 2.2.1 Problem setting
  • 2.2.2 Support vector machines
  • 2.2.3 Linear discriminant analysis
  • 2.2.4 Nearest neighbor classification
  • 2.2.5 Gaussian processes
  • 2.2.6 Neural networks
  • 2.2.7 Active learning
  • 2.3 Clustering
  • 2.3.1 k-means
  • 2.3.2 GaussianMixtureModels
  • 2.4 Metric Learning
  • 2.4.2 Duality between metric and feature learning
  • 2.4.3 Learning metrics for fixed features
  • 2.4.4 Deep metric learning
  • 2.5 Information retrieval
  • 2.5.1 Problem description
  • 2.5.2 Evaluation metrics
  • 2.5.3 Learning to rank
  • 2.5.4 System architecture
  • 2.5.5 Spatial verification and re-ranking
  • 2.5.6 Query expansion and diffusion
  • 2.5.7 Cross- and multi-modal retrieval
  • 2.6 Image representations for CBIR
  • 2.6.1 Hand-crafted local features
  • 2.6.2 Hand-crafted transformationsand aggregations
  • 2.6.3 Principal components analysis and whitening
  • 2.6.4 Off-the-shelf CNN features
  • 2.6.5 End-to-end learning for image retrieval
  • 2.7 Relevance feedback
  • 3 The Cosine Loss:A RetrievalMetricused for Classification
  • 3.1 Introduction and motivation
  • 3.1.1 The problem of small data
  • 3.1.2 Weakly supervised localization
  • 3.2 Related work
  • 3.2.2 Learning from small data
  • 3.2.3 Weakly supervised localization
  • 3.3 The cosine loss
  • 3.3.1 Objective and notation
  • 3.3.2 Comparison with other loss functions
  • 3.4 Dense classification andscene understanding
  • 4 Hierarchy-based SemanticImage Embeddings
  • 4.1 In the need of prior knowledge
  • 4.1.1 Semantic image retrieval
  • 4.1.2 Explaining classification decisions
  • 4.2 Related work
  • 4.3 Knowledge in trees: class taxonomies
  • 4.3.1 Hierarchy-based semantic similarity
  • 4.3.2 Tree-shaped taxonomies
  • 4.4 Hierarchy-based semantic embeddings
  • 4.4.1 Exact solution
  • 4.4.2 Low-dimensional approximation
  • 4.5 Learning semantic image embeddings
  • 4.6 Subsequent works onsemantic embeddings
  • 5 Experiments forCosine Loss and Semantic Embeddings
  • 5.1 Datasets
  • 5.1.1 Visual classification datasets
  • 5.1.2 FGVC datasets
  • 5.1.3 ExtremeWeather dataset
  • 5.1.4 AG News dataset
  • 5.1.5 MS COCO
  • 5.2 Training details
  • 5.3 Semantic image retrieval
  • 5.3.1 Performance metrics
  • 5.3.2 Competitors
  • 5.3.3 Semantic image retrieval performance
  • 5.3.4 Low-dimensional approximation
  • 5.4 Learning from small data
  • 5.4.1 Classification performance
  • 5.4.2 Effect of semantic information
  • 5.4.3 Effect of dataset size
  • 5.5 Learned feature space
  • 5.6 Dense classification
  • 5.6.1 Weakly supervised localization
  • 5.6.2 Explaining classifier decisions
  • 6 Interactive Image Retrieval
  • 6.1 Introduction

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