Inclusão de diversidade em consultas aos vizinhos mais próximos usando descritores distintos para similaridade e diversidade
Abstract
One of the ways to recover images in a database is through similarity queries. Using characteristics
extracted from these images, such as color, shape or texture, this work seeks to
identify similarities to a central query element. However, the results may be very similar to
each other, which is not always the expected result. In addition to the redundancy in the results,
the problem of the ’semantic gap’, which is a divergence in the evaluation of similarity
between images performed by the computer considering its numerical representation (low
level characteristics) and the human perception about the image (high level characteristics).
In order to improve the quality of the results, we sought to minimize the issue of redundancy
and the ’semantic gap’ through the use of more than one descriptor in queries for similarity.
We sought to explore the inclusion of diversity using one descriptor to treat similarity and
another descriptor to treat diversity, more generally a metric space for similarity and another
for diversity. For the implementation of the query by similarity was used the consultation
to several neighbors closer. Considering that the descriptors may be distinct and one of
them may have greater numerical representativeness, it was necessary to do the normalization,
considering the methods of normalization by the greater distance and normalization
by the greater approximate distance with balancing by the intrinsic dimension. An exhaustive
search algorithm was used to perform the tests. The experiments were carried out in a
classified database. To evaluate the semantic quality of the results, a measure was proposed
that evaluates the inclusion of diversity considering the diversity present in the query only
considering the similarity and the maximum diversity that can be included. A comparison
was made between the result obtained and the considered ideal, which refers to the value of
l defined by the user himself. By comparing the results obtained with the results obtained
in the queries for a single descriptor, the evaluation of the included diversity followed the
trend of l, which allows to say that normalization and balancing is necessary. In addition,
it is intended in the future to study new ways of normalizing.