Abstract:
Image search is more efficient for managing a wide range of image
databases. Content-based image retrieval (CBIR) is one of the image retrieval
techniques in which users use the visual characteristics of images such as color,
shape and texture, etc. It permits the end user to give a query image in order to
retrieve the image stored in the database based on the similarity to the query image.
The system extracts the features of the query image, searches the database for images
with similar features, and exhibits relevant images to the user in order of similarity to
the query. Many CBIR systems have been developed to compare, analyze, and search
images based on one or more of these features. This system is implemented as an
image retrieval system combining visual content features and a support vector
machine (SVM) classification.
First, the system extracts the features of images from dataset with color autocorrelogram, color moment and gabor wavelet for the training phrase. When the user
input query image, the system extracts features with these feature extraction methods
in the testing phrase. And then, the system applies support vector machine (SVM)
classifier to classify the image. After that, the system compares feature vectors
between the query image and image dataset. Finally, the system retrieves the
relevant image with query image. The applied system uses Wang dataset for the
purpose of training and testing the system. And other 100 images that are not from
dataset is also used for testing system. The overall accuracy of the system is over
80% for all classes. The system is implemented with MATLAB programming
language on window platform.
