Texture Representations for Content-based Retrieval
M.S. Thesis Kun Xu
Content-based image retrieval systems are of great interest today
because of the need for efficient ways to search large databases of
digital images. These systems employ low-level features such as
texture, color and shape to characterize the salient information in
the image, and the matching between the images is evaluated by
computing similarity measures.
Using texture as an example, we have investigated the quality of
representation provided by low-level features for the semantic meaning
of the image. We have shown that a more accurate description of the
underlying distribution of low-level features does not improve the
retrieval performance. We have also introduced a simplified
multiresolution symmetric autoregressive model to describe textures,
and a similarity measure based on the Bhattacharyya distance.
The effectiveness of the new autoregressive model and distance measure
was compared with that of the texture representations employed in the
literature: Wold, multiresolution simultaneous autoregressive (MRSAR)
model and Gabor filter bank, and the Mahalanobis distance based
similarity measure. Experiments were performed on all the available
texture databases: Brodatz, VisTex and MeasTex. The issue of
homogeneity of an image was examined by defining a database containing
the 50 perceptually most uniform images from the Brodatz database.
To facilitate the experimental work a prototype texture retrieval
system was developed which allows the user to search by using
different combinations of texture representations and distance
The thesis has part1 and part2. The size of the compressed files is about 12 M. The thesis contains 60 pages.
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