Heider K. Ali and Anthony Whitehead, Carleton University, Canada

An automatic mechanism for the selection of image subset of modern and historic images out of a landmark large image set collected from the internet is designed in this paper. This selection depends on the extraction of dominant features using Gabor filtering. These features are selected carefully from a preliminary image set and fed into a neural network as a training set. The mechanism collects a raw large set of landmark images containing modern and historic images and non-landmark images as well, process these images, and finally classify them as landmark and non-landmark images. The classification performance highly depends on the number of candidate features of the landmark.

Feature Extraction, Neural Networks, Gabor Filters, Subset Selection, Image Categorization