METHODS AND SYSTEMS FOR STYLE-BASED CLUSTERING OF ARTWORKS WITH PREFERENCE FEEDBACK

原文（中国語）

The disclosure relates generally to methods and systems for style-based clustering of artworks with preference feedback. Conventional techniques for artwork clustering rely on generic image representations derived from deep neural networks, thus heavily focused on content-level similarity rather than style-based similarity. According to the present disclosure, the plurality of artworks is passed through the artwork feature extractor to obtain the artwork features which are then passed to the autoencoder which encodes these features into lower dimension feature space. The clustering network layer employs the K-Means clustering algorithm to obtain the initial set of clusters. Then a preference feedback mechanism is employed with four operations: sample, expand, merge, and project, to obtain the style-based clusters. The sample operation facilitates the selection of samples for feedback. The preference feedback on the selected subset of the dataset is captured through the expand and merge operations which are projected onto the entire dataset.