A Comparative analysis of Texture feature and 3D colour Histogram for Content Based Image Retrieval.
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Date
2019-09-15
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Publisher
U6+ Consortium of African Universities Proceedings. Maiden Edition on Harnessing African Potentials for Sustainable Development, Calabar, Nigeria.
Abstract
The visual content of an image can be used to search for similar images based on user interest
from a large database, in the art known as Content Based Image Retrieval (CBIR). In CBIR
systems, the user presents a query image to the system, the system obtains the feature vector,
which is compared with certain image features of the images in the database. The adequacy of the
feature vectors extracted for the retrieval of appropriate and exact image from the database is an
open issue which calls for continual research and attention. This work involved the retrieval of
similar images based on the content of the image by comparing the feature vectors of the queried
image and those of the image database using Mahalanobis distance measure. Textural feature
vectors of the images were obtained using local binary pattern and 3D colour histogram feature
vectors were also extracted. The performances of these two different feature vectors were
compared based on the distance metrics to determine their suitability. The similar images in the
database are displayed along with their similarity distance value, in which the minimum distance
is a metric for the matched images. The CBIR system was implemented using a locally acquired
database of over 600 different images. Various images were used to query the CBIR system,
which was able to successfully output similar images. The simulation result obtained revealed
that textural feature vectors are more adequate in terms of speed and accuracy in content-based
image retrieval than 3D colour histogram based on the images used in the experiment.
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Citation
Babatunde, R. S., and Ajao, J. F. (2019)