  Impressive advances in acquisition and sharing technologies have made the
growth of multimedia collections and their applications almost unlimited.
However, the opposite is true for the availability of labeled data, which is
needed for supervised training, since such data is often expensive and
time-consuming to obtain. While there is a pressing need for the development of
effective retrieval and classification methods, the difficulties faced by
supervised approaches highlight the relevance of methods capable of operating
with few or no labeled data. In this work, we propose a novel manifold learning
algorithm named Rank Flow Embedding (RFE) for unsupervised and semi-supervised
scenarios. The proposed method is based on ideas recently exploited by manifold
learning approaches, which include hypergraphs, Cartesian products, and
connected components. The algorithm computes context-sensitive embeddings,
which are refined following a rank-based processing flow, while complementary
contextual information is incorporated. The generated embeddings can be
exploited for more effective unsupervised retrieval or semi-supervised
classification based on Graph Convolutional Networks. Experimental results were
conducted on 10 different collections. Various features were considered,
including the ones obtained with recent Convolutional Neural Networks (CNN) and
Vision Transformer (ViT) models. High effective results demonstrate the
effectiveness of the proposed method on different tasks: unsupervised image
retrieval, semi-supervised classification, and person Re-ID. The results
demonstrate that RFE is competitive or superior to the state-of-the-art in
diverse evaluated scenarios.
