| Description |
1 online resource (323 pages) |
| Physical Medium |
polychrome |
| Description |
text file |
| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Content-based image retrieval.
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Content-based image retrieval. |
| Genre/Form |
Electronic books.
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| Other Form: |
Print version: Barz, Björn Semantic and Interactive Content-based Image Retrieval Göttingen : Cuvillier Verlag,c2020 9783736973466 |
| ISBN |
3736963467 |
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9783736963467 (electronic book) |
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