Proceedings chapter

Visualizing weakly-Annotated Multi-label Mayan Inscriptions with Supervised t-SNE

Presented at Florence (Italy), 19th-21st June 2017
PublisherACM Press
Publication date2017

We present a supervised dimensionality reduction technique suitable for visualizing multi-label images on a 2-D space. This method extends the use of the well-known t-distributed stochastic embedding (t-SNE) algorithm to the case of multi-labels instances, where the concept of partial relevance plays an important role. Furthermore, it is applicable straightaway for weakly annotated data. We apply our approach to generate 2-D representations of Mayan glyph-blocks, which are groups of individual glyph-signs expressing full sentences. The resulting representations are used to place visual instances in a 2-D space with the purpose of providing a browsable catalog for further epigraphic studies, where nearby instances are similar both in semantic and visual terms. We evaluate the performance of our approach quantitatively by performing classification and retrieval experiments. Our results show that this approach obtains high performance in both of these tasks.

  • Information systems
  • Similarity measures
  • Relevance assessment
  • Applied computing
  • Media arts
  • Computing Methodologies
  • Semi-supervised learning settings
  • Dimensionality reduction
  • T-SNE
  • Partial relevance
  • Maya glyphs
Citation (ISO format)
ROMAN RANGEL, Edgar Francisco, MARCHAND-MAILLET, Stéphane. Visualizing weakly-Annotated Multi-label Mayan Inscriptions with Supervised t-SNE. In: Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing (CBMI ’17). Florence (Italy). [s.l.] : ACM Press, 2017. p. 6. doi: 10.1145/3095713.3095720
Main files (1)
Proceedings chapter (Published version)

Technical informations

Creation03/21/2018 5:02:00 PM
First validation03/21/2018 5:02:00 PM
Update time03/15/2023 8:01:14 AM
Status update03/15/2023 8:01:14 AM
Last indexation01/17/2024 2:32:39 AM
All rights reserved by Archive ouverte UNIGE and the University of GenevaunigeBlack