الفهرس 
Content-Based Image RetrievalOnly 14 pages are availabe for public view
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Abstract
Due to the enormous increase in image database sizes, as well
as its vast deployment in various applications, query by content
or Content Based Image Retrieval (CBIR) has recently been
proposed as an alternative to text-based retrieval for media
such as images, videos, and audios.
The main problems involved in text-based image retrieval
include: keyword annotation is labor intensive so it is hard to
index large sets of images using these annotations. Also, these
annotations are drawn from a predefined set of keywords,
which cannot cover all possible concepts images may represent
in addition that keywords assignment is subjective to the
person making it. To overcome these problems, content-based
image retrieval systems propose to index the media documents
based on features extracted from their content rather than by
textual annotations.
In content-based image retrieval, image data representation
and similarity measurement are two important tasks. So, when
building CBIR systems, this requires choosing and representing
the visual features and finding combinations of techniques that
give the best matches and enhance the performance of CBIR
systems.
This study aims to improve the performance of content-based
image retrieval systems via multiple features representations.
One way of doing this important task is to extract the visual
features from the database images and index them based on
these features, then examine the features extraction algorithms
and find good combinations of these algorithms by measuring
the retrieval accuracy when using them together On the other hand, the choice of the similarity distance
measures is also important as the retrieval process will be done
based on a comparison between the features vectors of the
query image and the corresponding ones of each image in the
database.
Using different categories of images (e.g. roses, people,
beach, and buses), the experiments confirmed the importance
of using the spatial information beside the color feature itself
and showed that: Haar and Daubechies wavelets can be
combined with Global Color Histogram (GCH) and the color
layout feature for efficient content-based image retrieval
systems. Also, the best retrieval accuracy is obtained when
combining the Haar wavelet with GCH in addition to the color
layout feature using the Euclidean distance measure, while also
the cosine similarity measure gives good results if compared
with the other used similarity measures like Manhattan and
correlation similarity measures.
The work presented here can be generalized and used in.