Department of Computer Science and Engineering
University of Washington
Object and Concept Recognition for Content-Based Image Retrieval
This reseach is funded by the National Science Foundation under
grant nr. IIS-0097329,
Project Summary
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 mechanism for
automating the development of recognizers for additional objects and
concepts. The resulting techniques will be evaluated on several
different large image databases, including
commercial databases whose images are grouped into broad classes and
a ground-truth database that provides a list of the objects in each
image. The results of this work will be a new generic object
recognition paradigm that can immediately be applied to automated or
semi-automated indexing of large image databases and will be a step
forward in object recognition.