On improving Content-Based Image Retrieval using Convolutional Neural Networks features (ISTI Grants for Young Mobility seminar series)

Day - Time: 23 November 2016, h.10:30
Place: Area della Ricerca CNR di Pisa - Room: C-29

Andrea Esuli


The research to effectively represent visual features of images has received much attention over the last decade. Although hand-crafted local features (e.g SIFTs and SURFs) and their encodings (e.g Bag of Words and Fisher Vectors) have shown high effectiveness in image classification and retrieval, the emerging deep Convolutional Neural Networks (CNN) have brought about breakthroughs in processing multimedia contents. In particular, the activations produced by an image within the intermediate layers of a CNN have been found notably effective as high-level image descriptors. This presentation will provide a comparison of several image representations for visual instance retrieval, including CNN features, Fisher Vectors, and their combinations. Two applications will be presented: the visual recognition of ancient inscriptions (such as Greco-Latin epigraphs), and the retrieval of human motions using RGB representations of spatio-temporal motion capture data.