IMPROVING
IMAGE
RETRIEVAL PERFORMANCE BY USING BOTH COLOR AND TEXTURE FEATURES
Dengsheng
Zhang
Email:
dengsheng.zhang@infotech.monash.edu.au
Content-based image retrieval (CBIR) is an
important multimedia application. In recently created MPEG-7 standard,
image
content is described by perceptual features such as color, texture and
shape.
Among these low level features, color features have been adopted in
most of the
image retrieval applications. Color feature is important and can be
conveniently extracted from images. However, image retrieval using
color
features often gives disappointing results because in many cases,
images with
similar colors do not have similar content. Color methods incorporating
spatial
information have been proposed to solve this problem, however, these
methods
often result in very high dimensions of features which drastically slow
down
the retrieval speed. In this paper, a method combining both color and
texture
features of image is proposed to improve the retrieval performance.
Given a
query, images in the database are firstly ranked using color features.
Then the
top ranked images are re-ranked according to their texture features.
Results
show the second process improves retrieval performance significantly.