Bio-medical Ontologies Maintenance and Change Management

74 L. Stanescu, D. Dan Burdescu, and M. Brezovan
image considered by the user as query [83]. The content-based visual query differs
from the usual query by the fact it implies the similarity search.
There are two forms of content-based visual query [83, 86]:
• The k-nearest neighborhood query – that retrieves the most appropriate k images
with the query image
• Range query – that retrieves all the images that respect a fixed limit of the similarity
between the target and the query image.
Next it is given the difference between the two utilization of the content-based
visual query, namely: content-based image query and content-based region query.
In the case of the content-based image query with the k- nearest neighborhood,
it is imposed to solve the following problem [83, 86]:
Being given a collection C of N images and a characteristics dissimilarity
function v f , it is required to find the k images α T ∈ C with the smallest
dissimilarity, v f (α Q , α T ) taking into consideration the query image α Q. .
In this case, a query always returns k images, which are usually sorted, in the
ascending order of the dissimilarity, taking into consideration the query image.
In the case of the content-based image query limited to a certain domain, it is
imposed to solve the problem [83, 86]:
Being given a collection C of N images and a characteristics dissimilarity
function v f , it is required to find the images α T ∈ C such that v f (α Q , α T ) ≤ σ f ,
where α Q is the query image, and σ f is a limit for the characteristics similarity.
In this case, the query returns a certain number of images, in function of the σ f
limit.
In a content-based region query, the images are compared on their regions. In
the first step of the query, there are made content-based visual queries on the
regions, and not on the images. In the final step of the query, there are determined
the images corresponding to the regions and there is computed the total distance
between the images by the weighting of the distances between regions. In the case
of this type of query it is imposed to solve the following problem [83, 86]:
Being given a collection C of N images and a features dissimilarity function vf,
is required to find the k images αT ∈ C that have at least R regions such that vf
(αQ R , αT R )≤ σf, where αQ R is the query image with R regions, and σf is a limit for
the characteristics similarity.
The content – based visual query may be improved by adding the spatial
information to the query. So, the total measure of the dissimilarity takes into
consideration both the features values (color, texture), and the spatial values of the
regions.
In present, there are developed techniques for spatial indexing that allow the
images retrieval based on their objects localization. These approaches compares
images were there were defined a-priori regions and objects.
There are two types of spatial indexing, namely: relative and absolute.
In the case of spatial relative indexing, the images are compared based on their
relative symbols locations. The following problem has to be solved [83, 86]: