Fusion in Computer Vision
Understanding Complex Visual Content
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Beschreibung
Visual content understanding is a complex and important challenge for applications in automatic multimedia information indexing, medicine, robotics, and surveillance. Yet the performance of such systems can be improved by the fusion of individual modalities/techniques for content representation and machine learning.
This comprehensive text/reference presents a thorough overview of Fusion in Computer Vision, from an interdisciplinary and multi-application viewpoint. Presenting contributions from an international selection of experts, the work describes numerous successful approaches, evaluated in the context of international benchmarks that model realistic use cases at significant scales.
Topics and features:
- Examines late fusion approaches for concept recognition in images and videos, including the bag-of-words model
- Describes the interpretation of visual content by incorporating models of the human visual system with content understanding methods
- Investigates the fusion of multi-modal features of different semantic levels, as well as results of semantic concept detections, for example-based event recognition in video
- Proposes rotation-based ensemble classifiers for high-dimensional data, which encourage both individual accuracy and diversitywithin the ensemble
- Reviews application-focused strategies of fusion in video surveillance, biomedical information retrieval, and content detection in movies
- Discusses the modeling of mechanisms of human interpretation of complex visual content
This authoritative collection is essential reading for researchers and students interested in the domain of information fusion for complex visual content understanding, and related fields.
This book presents a thorough overview of fusion in computer vision, from an interdisciplinary and multi-application viewpoint, describing successful approaches, evaluated in the context of international benchmarks that model realistic use cases. Features: examines late fusion approaches for concept recognition in images and videos; describes the interpretation of visual content by incorporating models of the human visual system with content understanding methods; investigates the fusion of multi-modal features of different semantic levels, as well as results of semantic concept detections, for example-based event recognition in video; proposes rotation-based ensemble classifiers for high-dimensional data, which encourage both individual accuracy and diversity within the ensemble; reviews application-focused strategies of fusion in video surveillance, biomedical information retrieval, and content detection in movies; discusses the modeling of mechanisms of human interpretation of complex visual content.
Examines information fusion in the context of multimodal and multidimensional data representation, i.e., video, image and text Presents a focus on information fusion for tackling higher-level description of multimedia information Discusses the latest research on a broad range of multimedia information fusion techniques Includes supplementary material: sn.pub/extras
Autor*in
Bogdan Ionescu
Themen in »Fusion in Computer Vision«
Bagging and Boosting Methods Contextual Fusion Data Dimensionality Reduction Early Fusion Feature Fusion Hierarchical and Community-Based Fusion Kernel Fusion Late Fusion Legal, Ethical and Social Concepts of Fusion Metric Spaces Multimodal Fusion Normalization for Fusion Social Media Fusion and Mining