Active learning for multimedia

ABSTRACT

Active learning improves the performance of classification or search systems by adding humans to the loop. It aims at optimizing the production of the class labels that are necessary for supervised learning. The proposed tutorial responds to a strong need for the integration of this technique in multimedia indexing and retrieval systems. It presents the basics of active learning and gives the necessary information for quickly and efficiently integrating it within a project. Several applications are considered, from relevance feedback to corpus annotation. Most illustrations are given in the context of the NIST benchmarks on video indexing and retrieval (TRECVID).

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Abstracting methods

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Indexing methods

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Concept learning
  I.6 SIMULATION AND MODELING
      I.6.4 Model Validation and Analysis

General Terms:
Algorithms, Experimentation, Human Factors, Performance

Keywords:
active learning, multimedia indexing