  Due to the swift growth of patent applications each year, information and
multimedia retrieval approaches that facilitate patent exploration and
retrieval are of utmost importance. Different types of visualizations (e.g.,
graphs, technical drawings) and perspectives (e.g., side view, perspective) are
used to visualize details of innovations in patents. The classification of
these images enables a more efficient search and allows for further analysis.
So far, datasets for image type classification miss some important
visualization types for patents. Furthermore, related work does not make use of
recent deep learning approaches including transformers. In this paper, we adopt
state-of-the-art deep learning methods for the classification of visualization
types and perspectives in patent images. We extend the CLEF-IP dataset for
image type classification in patents to ten classes and provide manual ground
truth annotations. In addition, we derive a set of hierarchical classes from a
dataset that provides weakly-labeled data for image perspectives. Experimental
results have demonstrated the feasibility of the proposed approaches. Source
code, models, and dataset will be made publicly available.
