Authors:
Afra'a Ahmad Alyosef
and
Andreas Nürnberger
Affiliation:
Otto von Guericke University Magdeburg, Germany
Keyword(s):
SIFT Descriptor, RC-SIFT 64D, Feature Truncating, Properties of the SIFT Features, Image Near Duplicate
Retrieval.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Feature Selection and Extraction
;
Geometry and Modeling
;
Image Understanding
;
Image-Based Modeling
;
Pattern Recognition
;
Software Engineering
;
Theory and Methods
Abstract:
The scale invariant feature transformation algorithm (SIFT) has been widely used for near-duplicate retrieval
tasks. Most studies and evaluations published so far focused on increasing retrieval accuracy by improving
descriptor properties and similarity measures. Contrast, scale and orientation properties of the SIFT features
were used in computing the SIFT descriptor, but their explicit influence in the feature matching step was not
studied. Moreover, it has not been studied yet how to specify an appropriate criterion to extract (almost) the
same number of SIFT features (respectively keypoints) of all images in a database. In this work, we study the
effects of contrast and scale properties of SIFT features when ranking and truncating the extracted descriptors.
In addition, we evaluate if scale, contrast and orientation features can be used to bias the descriptor matching
scores, i.e., if the keypoints are quite similar in these features, we enforce a higher similarity in descriptor
matching. We provide results of a benchmark data study using the proposed modifications in the original
SIFT128D and on the region compressed SIFT (RC-SIFT64D) descriptors. The results indicate that using
contrast and orientation features to bias feature matching can improve near-duplicate retrieval performance.
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