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 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.
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.
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