Performance Analysis in Content-based Retrieval
with Textures
Kun Xu(1), Bogdan Georgescu(2), Dorin Comaniciu(3), Peter Meer(1)
(1)Department of Electrical and Computer Engineering
(2)Department of Computer Science
Rutgers University, Piscataway, NJ 08855, USA
(3) Imaging & Visualization Department
Siemens Corporate Research
755 College Road East
Princeton, NJ 08540
The features employed in content-based retrieval are most often simple
low-level representations, while a human observer judges similarity
between images based on high-level semantic properties. Using textures
as an example, we show that a more accurate description of the underlying
distribution of low-level features does not improve the retrieval
performance. We also introduce the simplified multiresolution
symmetric autoregressive model for textures, and the Bhattacharyya distance
based similarity measure. Experiments are performed with four texture
representations and four similarity measures over the Brodatz and
VisTex databases.
15th International Conference on Computer Vision and
Pattern Recognition , September 2000, Barcelona, Spain, vol.
IV, 275-278.
