DEVELOPING A MODEL SEMANTIC-BASED IMAGE RETRIEVAL BY COMBINING KD-TREE
STRUCTURE WITH ONTOLOGY
Abstract
The paper proposes an alternative approach to improve the
performance of image retrieval. In this work, a framework for image
retrieval based on machine learning and semantic retrieval is proposed.
In the preprocessing phase, the image is segmented objects by using
Graph-cut, and the feature vectors of objects presented in the image and
their visual relationships are extracted using R-CNN. The feature
vectors, visual relationships, and their symbolic labels are stored in
KD-Tree data structures which can be used to predict the label of
objects and visual relationships later. To facilitate semantic query,
the images use the RDF data model and create an ontology for the
symbolic labels annotated. For each query image, after extracting their
feature vectors, the KD-Tree is used to classify the objects and predict
their relationship. After that, a SPARQL query is built to extract a set
of similar images. The SPARQL query consists of triple statements
describing the objects and their relationship which were previously
predicted. The evaluation of the framework with the MS-COCO dataset and
Flickr showed that the precision achieved scores of 0.9218 and 0.9370
respectively.
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