Deep Descriptor Learning with Auxiliary Classification Loss for Retrieving Images of Silk Fabrics in the Context of Preserving European Silk Heritage
Bibliographic Data
| ID | 22032489 |
|---|---|
| Authors | Mareike Dorozynski (0000-0002-1293-6039, Leibniz University Hannover, corresponding author), Franz Rottensteiner (0000-0003-1942-8210, Leibniz University Hannover) |
| Year | 2022 |
| Volume | 11 |
| Issue | 2 |
| Pages | 82 |
| Publication date | 2022-01-21 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi11020082 |
| OpenAlex | W4206896999 |
| Language | EN |
| References cited | 59 |
With the growing number of digitally available collections consisting of images depicting relevant objects from the past in relation with descriptive annotations, the need for suitable information retrieval techniques is becoming increasingly important to support historians in their work. In this context, we address the problem of image retrieval for searching records in a database of silk fabrics. The descriptors, used as an index to the database, are learned by a convolutional neural network, exploiting the available annotations to automatically generate training data. Descriptor learning is combined with auxiliary classification loss with the aim of supporting the clustering in the descriptor space with respect to the properties of the depicted silk objects, such as the place or time of origin. We evaluate our approach on a dataset of fabric images in a kNN-classification, showing promising results with respect to the ability of the descriptors to represent semantic properties of silk fabrics; integrating the auxiliary loss improves the overall accuracy by 2.7% and the average F1 score by 5.6%. It can be observed that the largest improvements can be obtained for variables with imbalanced class distributions. An evaluation on the WikiArt dataset demonstrates the transferability of our approach to other digital collections
Cluster analysis · Convolutional neural network · Deep learning · Geography · Information retrieval · SILK · Advanced Image and Video Retrieval Techniques · Computer Science · Genomics and Phylogenetic Studies · Multimodal Machine Learning Applications · Artificial Intelligence
| Citation velocity | historical |
|---|---|
| Highly cited | No |