  Information from an image occurs over multiple and distinct spatial scales.
Image pyramid multiresolution representations are a useful data structure for
image analysis and manipulation over a spectrum of spatial scales. This paper
employs the Gaussian-Laplacian pyramid to treat different spatial frequency
bands of a texture separately. First, we generate three images corresponding to
three levels of the Gaussian-Laplacian pyramid for an input image to capture
intrinsic details. Then we aggregate features extracted from gray and color
texture images using bio-inspired texture descriptors, information-theoretic
measures, gray-level co-occurrence matrix features, and Haralick statistical
features into a single feature vector. Such an aggregation aims at producing
features that characterize textures to their maximum extent, unlike employing
each descriptor separately, which may lose some relevant textural information
and reduce the classification performance. The experimental results on texture
and histopathologic image datasets have shown the advantages of the proposed
method compared to state-of-the-art approaches. Such findings emphasize the
importance of multiscale image analysis and corroborate that the descriptors
mentioned above are complementary.
