Image Subset Selection Using Gabor Filters and Neural Networks

TL;DR

This paper introduces an automated approach combining Gabor filters and neural networks to select and classify landmark images from large internet datasets, improving landmark recognition accuracy.

Contribution

The paper presents a novel method that integrates Gabor feature extraction with neural network classification for effective image subset selection.

Findings

High classification accuracy achieved for landmark images

Effective feature selection improves neural network performance

Method successfully distinguishes landmark from non-landmark images

Abstract

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.