Researchers teach computers how to name images by 'thinking'
Wednesday, November 1, 2006
University Park, Pa. -- Penn State researchers have "taught"
computers how to interpret images using a vocabulary of up to 330
English words, so that a computer can describe a photograph of two polo
players, for instance, as "sport," "people," "horse," "polo."
The new system, which can automatically annotate entire online
collections of photographs as they are uploaded, means significant
time-savings for the millions of Internet users who now manually tag or
identify their images. It also facilitates retrieval of images through
the use of search terms, said James Wang, associate professor in the
Penn State College of Information Sciences and Technology, and one of
the technology's two inventors.
The system is described in a paper, "Real-Time Computerized
Annotation of Pictures," given at the recent ACM Multimedia 2006
conference in Santa Barbara, Calif., and authored by Jia Li, associate
professor, Department of Statistics, and Wang. Penn State has filed a
provisional patent application on the invention.
Major search engines currently rely upon uploaded tags of text to
describe images. While many collections are annotated, many are not.
The result: Images without text tags are not accessible to Web
searchers. Because it provides text tags, the ALIPR system -- Automatic
Linguistic Indexing of Pictures-Real Time -- makes those images visible
to Web users.
ALIPR does this by analyzing the pixel content of images and
comparing that against a stored knowledge base of the pixel content of
tens of thousands of image examples. The computer then suggests a list
of 15 possible annotations or words for the image.
"By inputting tens of thousands of images, we have trained computers
to recognize certain objects and concepts and automatically annotate
those new or unseen images," Wang said. "More than half the time, the
computer's first tag out of the top 15 tags is correct."
In addition, for 98 percent of images tested, the system has
provided at least one correct annotation in the top 15 selected words.
The system, which completes the annotation in about 1.4 seconds, also
can be applied to other domains such as art collections, satellite
imaging and pathology slides, Wang said.
The new system builds on the authors' previous invention, ALIP,
which also analyzes image content. But unlike ALIP which characterized
images by incorporating computational-intensive spatial modeling, ALIPR
characterizes images by modeling distributions of color and texture.
The researchers acknowledge computers trained with their algorithms
have difficulties when photos are fuzzy or have low contrast or
resolution; when objects are shown only partially; and when the angle
used by the photographer presents an image in a way that is different
than how the computer was trained on the object. Adding more training
images as well as improving the training process may reduce these
limitations -- future areas of research.
A demonstration of the ALIPR system can be found at http://www.alipr.com online.
In a companion paper also presented at the ACM conference, the
researchers describe another of their systems-one that can use
annotations in a retrieval process. This new system leverages
annotations from different sources, human and computer. The
researchers, who have built a prototype of the system, are working on
testing it in real-world situations. That paper, "Toward Bridging the
Annotation-Retrieval Gap in Image Search by a Generative Modeling
Approach," was authored by Ritendra Datta and Weina Ge, Ph.D. students
in computer science and engineering; Li; and Wang.
"Our approach aims at making all pictures on the Internet visible to the users of search engines," Wang said.
Research on both systems was supported by the National Science Foundation.