Fast identification of visual documents using local descriptors
Proceedings of the eighth ACM symposium on Document engineering
High-dimensional descriptor indexing for large multimedia databases
Proceedings of the 17th ACM conference on Information and knowledge management
| Hi-index | 0.00 |
The task of identifying an image whose metadata are missing is often demanded from cultural image collections holders, such as museums and archives. The query image may present distortions (cropping, rescaling rotations, colour changes, noise...) from the original, which poses an additional complication. The majority of proposed solutions are based on classic image signatures, such as the colour histogram. Our approach, however, follows computer vision methods, and is based on local descriptors. In this paper we describe our approach, explain the SIFT method on which it is based and compared it to the Multiscale-CCV, an established scheme employed in a large scale practical system. We demonstrate experimentally the efficacy of our approach, which achieved a 99,2% success rate, against 61,0% for the Multiscale-CCV, in a database of photos, drawings and paintings.