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图像处理_Object and Concept Recognition for Content-Based Image Retrieval(华盛顿大学基于内容的检索数
Object and Concept Recognition for Content-Based
Image Retrieval(华盛顿大学基于内容的检索数据库)
数据摘要：
With the advent of powerful but inexpensive computers and storage
devices and with the availability of the World Wide Web, image databases
have moved from research to reality. Search engines for finding images
are available from commercial concerns and from research institutes.
These search engines can retrieve images by keywords or by image
content such as color, texture, and simple shape properties. Content-based
image retrieval is not yet a commercial success, because most real users
searching for images want to specify the semantic class of the scene or
the object(s) it should contain. The large commercial image providers are
still using human indexers to select keywords for their images, even
though their databases contain thousands or, in some cases, millions of
images. Automatic object recognition is needed, but most successful
computer vision object recognition systems can only handle particular
objects, such as industrial parts, that can be represented by precise
geometric models. Content-based retrieval requires the recognition of
generic classes of objects and concepts. A limited amount of work has
been done in this respect, but no general methodology has yet emerged.
The goal of this research is to develop the necessary methodology for
automated recognition of generic object and concept classes in digital
images. The work will build on existing object-recognition techniques in
computer vision for low-level feature extraction and will design
higher-level relationship and cluster features and a new unified
recognition methodology to handle the difficult problem of recognizing
classes of objects, instead of particular instances. Local feature
representations and global summaries that can be used by
general-purpose classifiers will be developed. A powerful new
hierarchical multiple classifier methodology will provide the learning
mech
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