Introduction to SALIC

Title:

Introduction to SALIC

Access Right:

Open Access

Created on:

08/08/2016

Language:

English

Keywords:

Metadata extraction, Metadata processing

Contributor:

Preserveware Editor

Date:

15/05/2017

Metrics:

899 views , 8 Downloads
A software toolbox designed for use and adaptation by machine learning researchers and developers working in the field of automated image annotation and classification.

Expected learning outcomes

Gaining a deeper understanding of principles of object annotation with SALIC (also known as PeriCoDe in connection with PERICLES use cases).

What is the target audience?

  • Digital Preservation professionals and students who are interested in learning more about cutting edge technologies in this area, but who are ultimately interested in the application of these tools into their own work.
  • Researchers exploring solutions for data management, digital preservation and image annotation
  • Teachers/trainers in this field
  • Solution providers for organisations in demand of solutions for data / repository management and digital preservation

Level of advancement/ prerequisites

Begin to gain a more advanced understanding of image annotation and approaches to developing training data sets.

Material chapters

  1. Background to PeriCoDe
  2. Installation and requirements
  3. Using PeriCoDe

Time required for completion

8 hours (incl. reading list)

​Supplementary reading

More detailed information about SALIC can be found in:

E. Chatzilari, S. Nikolopoulos, Y. Kompatsiaris and J. Kittler, “SALIC: Social Active Learning for Image Classification,” in IEEE Transactions on Multimedia, vol. 18, no. 8, pp. 1488-1503, Aug. 2016. (URL:http://dx.doi.org/10.1109/TMM.2016.2565440)
Maronidis, A. et al. (2016). PERICLES Deliverable D4.3: Content Semantics and Use Context Analysis Techniques. http://pericles-project.eu/uploads/files/PERICLES_WP4_D4_3_Content_Semantics_and_Use_Context_Analysis_Techniques_V1.pdf

Although PeriCoDe addresses a very important problem in automatic image annotation, the collection of annotated training data is only one aspect of the process: the other part is the training of a classifier using these data in order to annotate – or classify – unseen images. This is known as machine learning and is performed in the test implementation of SALIC’s wrapper.m – to learn more about this, the interested reader should explore the following (non exhaustive list of) resources:

https://www.coursera.org/learn/machine-learning

https://darshanhegde.wordpress.com/2014/08/19/learn-machine-learning-the-hard-way/

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