Abstract:
Image retrieval is increasingly becoming an interesting filed of
research as the images that users store and process keep on rising
both in number and size especially in digital databases. The
images are stored on portable devices which users have used to
capture these images.
The aim of this research is to solve the issues experienced by
users in image retrieval of digital images stored in their devices,
ensuring that images requested are retrieved accurately from
storage. The images are pre-processed to remove noise and
refocus images to enhance mage content. The image retrieval is
based on the content (Content Based Image Retrieval) where
images are matched in a database based on the subject of the
image.
In this research, Corel image database is used with image pre-
processing to ensure that image subjects are enhanced. Images
are placed in classes and images are retrieved based on the users
input. Euclidean distance method is used to determine the nearest
objects, thus resulting in the least number of images retrieved by
the system. Colour and texture features are used to generate the
feature matrices on which the image comparison is made. For
KNN algorithm, different values of K will be tested to determine
best value for different classes of images. The performance of the
design is compared to MATLAB image retrieval system using the
same image data set.
The results obtained show that the combination of colour, texture
and KNN in image retrieval results in shorter computation time
as compared to the performance of individual methods
