Abstract: Visual information retrieval (VIR) is an active and vibrant research area which attempts at providing means for organizing, indexing, annotating, and retrieving visual information (images and videos) form large, unstructured repositories. In its early years (1995-2000) the research efforts were dominated by content-based approaches contributed primarily by the image and video processing community. Later, it was widely recognized that the challenges imposed by the semantic gap (the lack of coincidence between an image’s visual contents and its semantic interpretation) required a clever use of textual metadata (in addition to information extracted from the image’s pixel contents) to make image and video retrieval solutions efficient and effective. The need to bridge (or at least narrow) the semantic gap has been one of the driving forces behind current VIR research. Additionally, other related research problems and market opportunities have started to emerge, offering a broad range of exciting problems for computer scientists and engineers to work on.

This talk revisits the field of content-based image retrieval (CBIR) 10 years after “the end of the early years” (as announced in a seminal paper in the field) and highlights the most relevant advances, pending challenges, and promising opportunities in CBIR and related areas. Particularly, it includes an overview of the important field of medical image retrieval, its main challenges and opportunities.