Progress in multimedia analysis
The same point also helps in full text retrieval. When texts are included as
counter examples in the query, the computer may be able to determine the
proper response much more quickly.
For query by example, a distinction should be made between external examples
brought in by the user and internal examples where the user has selected an item
from the database. When the example is external, in practice the query example
is not annotated, so the system can only search for similar items on the basis of
the content descriptors described above. When the example is internal,
similarity can also be based on the annotations of the items.
In practice the user will not get the answer directly from one of the above query
types, but will engage in an interactive session with the system where advanced
visualization and relevance feedback from the user are iteratively used to bring
the user closer to the desired information. Ideally, the system is actively
participating in finding the best solution by posing the most informative
questions or showing the most informative results to the user.
Figure 3. Example of an advanced visualization tool where the user gives
feedback to the system by indicating relevant and non-relevant items.
Interactivity poses heavy demands on the computing, storage, and display
capacity of the system. Users want immediate feedback on their queries, but this
might require computing a large set of relevant descriptors if external examples
are used, and then requires comparing the descriptors of all elements in the
dataset with the query. Advanced database techniques are required to limit the
search. In addition, interactive search stretches the functionality of the
presentation devices to the limit. Nevertheless, interactivity compensates for the
inability of the computer to take account of context. In a full interaction
scheme, not only the query may be modified but also what is to be considered
similar, and what are to be considered good examples and counter examples. By
using relevance feedback and visual presentation of the best results (see the
figure), current content-based retrieval systems only scratch the surface of what
is to be expected in the near future.
ARNOLD W.M SMEULDERS, FRANCISKA DE JONG AND MARCEL WORRING MULTIMEDIA INFORMATION
TECHNOLOGY AND THE ANNOTATION OF VIDEO
In this section we review the state of the art in multimedia information analysis
disciplines: computer vision, text processing, and audio processing, followed by
interaction and machine learning.
Computer vision started in the sixties with occasional pictures of space and
medical images. Processing was concentrated on large computers. In the early
nineties, personal computers became sufficiently powerful to hold a digital image,
popularizing picture computation. Digital storage of pictures, and family
communication with pictures through the internet, followed later. Digital image
sensors are now found in many devices. It is estimated that more than half of all
new cameras are digital as well as a quarter of all family video devices. Hence,
computer vision has developed from an esoteric science to a necessary ingredient
of the information society in just 15 years.
An essential step forward was the recognition that precise segmentation of an
object in the foreground against the background is unattainable. There is
evidence that even humans break down images into named objects only when
necessary. To identify a scene, it may be sufficient to recognize just a few details. A
typical example is an orange circle somewhere in the middle of a picture
signifying a setting sun. Another typical example relating to texture is a patch of
striped skin immediately identifying the presence of a tiger or a zebra. And a
typical example of a characteristic spatial arrangement is a face. Now it can be
understood why Hawaiian sunsets, faces, and tigers are frequently used in demon
strations of video search systems. But, it requires more progress to develop their
success into a general capability of recognizing items in any image [Fergus 2003],
Data definition annotation
Figure 4. Sketch of the flow of information
in a system for interactive visual annotation
and query by example.