With the improving capabilities of current hardware systems, there
are ever growing possibilities to store and manipulate videos in a
digital format, leading to a growing number of video archives.
People build their own digital libraries from materials created
through digital cameras and camcorders, and use systems such as
YouTube to place this material on the web. Unfortunately, this data
creation prowess is not matched by any comparable tools to organise
and retrieve video information. There is a need to create new
retrieval engines to assist the users in searching and finding
video scenes they would like to see from many different video
files. Unlike text retrieval systems, retrieval on digital video
datasets is facing a serious problem: The Semantic Gap. This is the
difference between low-level data representation of videos and the
higher level concepts user associates with video. This book
introduces several approaches to bridge this semantic gap, explains
different evaluation strategies and presents state-of-the-art video
retrieval tools. The target audience is everyone who is interested
in getting to know the research approaches that led to the popular
video retrieval tools.
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