Name
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. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Training set,Computer vision,Pattern recognition,Computer science,Filter (signal processing),Artificial intelligence,Landmark,Artificial neural network,Machine learning,The Internet |
DocType | Volume | Citations |
Journal | abs/1504.01954 | 0 |
PageRank | References | Authors |
0.34 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heider K. Ali | 1 | 0 | 0.68 |
| Anthony Whitehead | 2 | 143 | 20.84 |