Exquisitor: Breaking the Interaction Barrier for Exploration of 100 Million Images

International audienceIn this demonstration, we present Exquisitor, a media explorer capable of learning user preferences in real-time during interactions with the 99.2 million images of YFCC100M. Exquisitor owes its efficiency to innovations in data representation, compression, and indexing. Exquisitor can complete each interaction round, including learning preferences and presenting the most relevant results, in less than 30 ms using only a single CPU core and modest RAM. In short, Exquisitor can bring large-scale interactive learning to standard desktops and laptops, and even high-end mobile devices

Exquisitor at the Lifelog Search Challenge 2020

We present an enhanced version of Exquisitor, our interactive and scalable media exploration system. At its core, Exquisitor is an interactive learning system using relevance feedback on media items to build a model of the users' information need. Relying on efficient media representation and indexing, it facilitates real-time user interaction. The new features for the Lifelog Search Challenge 2020 include support for timeline browsing, search functionality for finding positive examples, and significant interface improvements. Participation in the Lifelog Search Challenge allows us to compare our paradigm, relying predominantly on interactive learning, with more traditional search-based multimedia retrieval systems

VLS-MCMR'10 - Proceedings of the 2010 ACM International Workshop on Very-Large-Scale Multimedia Corpus, Mining and Retrieval, Co-located with ACM Multimedia 2010: Foreword

VLS-MCMR'10 - Proceedings of the 2010 ACM International Workshop on Very-Large-Scale Multimedia Corpus, Mining and Retrieval, Co-located with ACM Multimedia 2010iii-i

Interactive Learning for Multimedia at Large

International audienceInteractive learning has been suggested as a key method for addressing analytic multimedia tasks arising in several domains. Until recently, however, methods to maintain interactive performance at the scale of today's media collections have not been addressed. We propose an interactive learning approach that builds on and extends the state of the art in user relevance feedback systems and high-dimensional indexing for multimedia. We report on a detailed experimental study using the ImageNet and YFCC100M collections, containing 14 million and 100 million images respectively. The proposed approach outperforms the relevant state-of-the-art approaches in terms of interactive performance, while improving suggestion relevance in some cases. In particular, even on YFCC100M, our approach requires less than 0.3 s per interaction round to generate suggestions, using a single computing core and less than 7 GB of main memory