Challenging deep image descriptors for retrieval in heterogeneous iconographic collections
This work addresses retrieval problems for cultural heritage collections, but it is incremental as it evaluates existing methods without introducing new techniques.
The paper studied how state-of-the-art deep learning image descriptors perform in content-based image retrieval on heterogeneous cultural image datasets with complex variations like multi-source and multi-view data, finding that these descriptors face significant challenges in such environments.
This article proposes to study the behavior of recent and efficient state-of-the-art deep-learning based image descriptors for content-based image retrieval, facing a panel of complex variations appearing in heterogeneous image datasets, in particular in cultural collections that may involve multi-source, multi-date and multi-view Permission to make digital