However, research on content-based image retrieval is still in its very early stages. We now briefly describe our recent work on using texture information for image retrieval. Examples of texture images include photographs of water, sand, a brick wall, a wire fence, or aerial photographs of agricultural regions. For example, consider a bunch of coffee beans spread on the ground. While each bean is clearly an identifiable object, the random distribution of the beans as a whole is more like a texture pattern.
Natural textures tend to be more irregular than man made ones.
During the past six months, we have made considerable progress in developing algorithms for texture based search. The basic idea is to pre-process the images at the time of storage and extract the texture information. This is done using Gabor filters, which are modulated Gaussians. Simple statistical moments such as the mean and standard deviation of the filtered outputs can now be used as indices to search the database. Instead of Gabor filters, one may also use the same orthogonal wavelet transform that was used for storing the image data.