Enhanced Retrieval and Browsing in the IMOTION System

  • Luca Rossetto
  • Ivan Giangreco
  • Claudiu Tănase
  • Heiko Schuldt
  • Stéphane Dupont
  • Omar Seddati
Conference paper

DOI: 10.1007/978-3-319-51814-5_43

Volume 10133 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Rossetto L., Giangreco I., Tănase C., Schuldt H., Dupont S., Seddati O. (2017) Enhanced Retrieval and Browsing in the IMOTION System. In: Amsaleg L., Guðmundsson G., Gurrin C., Jónsson B., Satoh S. (eds) MultiMedia Modeling. MMM 2017. Lecture Notes in Computer Science, vol 10133. Springer, Cham

Abstract

This paper presents the IMOTION system in its third version. While still focusing on sketch-based retrieval, we improved upon the semantic retrieval capabilities introduced in the previous version by adding more detectors and improving the interface for semantic query specification. In addition to previous year’s system, we increase the role of features obtained from Deep Neural Networks in three areas: semantic class labels for more entry-level concepts, hidden layer activation vectors for query-by-example and 2D semantic similarity results display. The new graph-based result navigation interface further enriches the system’s browsing capabilities. The updated database storage system \(\textsf {ADAM}_{{pro }}\) designed from the ground up for large scale multimedia applications ensures the scalability to steadily growing collections.

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Luca Rossetto
    • 1
  • Ivan Giangreco
    • 1
  • Claudiu Tănase
    • 1
  • Heiko Schuldt
    • 1
  • Stéphane Dupont
    • 2
  • Omar Seddati
    • 2
  1. 1.Databases and Information Systems Research Group, Department of Mathematics and Computer ScienceUniversity of BaselBaselSwitzerland
  2. 2.Research Center in Information TechnologiesUniversité de MonsMonsBelgium