Cataloged from PDF version of article.In recent years, very large collections of images and videos have grown rapidly.
In parallel with this growth, content-based retrieval and querying the indexed collections
are required to access visual information. Two of the main components of
the visual information are texture and color. In this thesis, a content-based image
retrieval system is presented that computes texture and color similarity among
images. The underlying technique is based on the adaptation of a statistical approach
to texture analysis. An optimal set of five second-order texture statistics
are extracted from the Spatial Grey Level Dependency Matrix of each image, so
as to render the feature vector for each image maximally informative, and yet
to obtain a low vector dimensionality for efficiency in computation. The method
for color analysis is the color histograms, and the information captured within
histograms is extracted after a pre-processing phase that performs color transformation,
quantization, and filtering. The features thus extracted and stored within
feature vectors are later compared with an intersection-based method. The system
is also extended for pre-processing images to segment regions with different
textural quality, rather than operating globally over the whole image. The system
also includes a framework for object-based color and texture querying, which
might be useful for reducing the similarity error while comparing rectangular regions
as objects. It is shown through experimental results and precision-recall
analysis that the content-based retrieval system is effective in terms of retrieval
and scalability.Konak, Eyüp SabriM.S
