Databases Reference
In-Depth Information
temporal composition is defined in terms of directed acyclic graphs, in which
the nodes are objects and the edges represent temporal relations. The con-
cepts of qualitative and quantitative inconsistency are introduced. The first
concept is related to the incompatibility of a set of temporal relations, and
the second concept is related to the relations that arise from the errors that
occur due to the specific durations of media objects.
8.4.1.3 Content-Based Retrieval
The retrieval of multimedia information from DBs is evolving as a challeng-
ing research and industrial area. There is already a substantial volume of
results in both levels. This section reviews important efforts in this topic, spe-
cifically research for image and video retrieval based on content.
Image Retrieval
Image retrieval is concerned with retrieving images relevant to users queries
from a large image collection. The relevance is determined by the nature of
the application. For instance, in a fabric-image DB, relevant images would be
those matching a sample in terms of texture and color. In a news photogra-
phy DB, date, time, and the occasion at which the photograph was taken
may be just as important as the actual visual content. Many relational DB
systems support fields for binary large objects (BLOBs) and facilitate access
by user-defined attributes such as date, time, media type, image resolution,
and source. On the other hand, content-based systems analyze the visual
content of images and index extracted features.
Possible query categories involving one or more features are proposed
in [25].
Simple visual feature query. The user specifies certain values possibly
with percentages for a feature. Example: Retrieve images which
contain 70 percent blue, 20 percent red, 30 percent yellow.
·
Feature combination query. The user combines different features and
specifies their values and weights. Example: Retrieve images with
green color and tree texture where color has weight 75 percent and
texture has weight 25 percent.
·
Localized feature query. The user specifies feature values and loca-
tions by placing regions on a canvas. Example: Retrieve images
with sky blue at the upper half and green at the bottom half.
·
Query by example. The system generates a random set of images. The
user selects one image and retrieves similar images. Similarity can be
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