Image Subset Selection Using Gabor Filters and Neural Networks
This work addresses image subset selection for landmark identification, but it appears incremental as it combines existing techniques like Gabor filters and neural networks without claiming major breakthroughs.
The paper tackles the problem of automatically selecting landmark images from a mixed set of modern, historic, and non-landmark images by using Gabor filters for feature extraction and neural networks for classification, achieving performance that depends on the number of candidate features.
An automatic method for the selection of subsets of images, both modern and historic, out of a set of landmark large images collected from the Internet is presented in this paper. This selection depends on the extraction of dominant features using Gabor filtering. Features are selected carefully from a preliminary image set and fed into a neural network as a training data. The method collects a large set of raw landmark images containing modern and historic landmark images and non-landmark images. The method then processes these images to classify them as landmark and non-landmark images. The classification performance highly depends on the number of candidate features of the landmark.