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Research & Integration


WP2: Evaluation, Integration and Standards
WP3: Visual Content Indexing
WP4: Content Description for Audio, Speech and Text
WP5: Multimodal Processing and Interaction
WP6: Machine Learning and Computation Applied to Multimedia
WP7: Dissemination towards Industry

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An Interactive Content-Based Image Retrieval System

Content-Based Image Retrieval (CBIR) systems have attracted large amounts of research attention since 1990's. Contrary to the early systems, focused on full-automatic strategies, recent approaches introduce human-computer interaction into CBIR. RETIN is the on-line image search system developed in the ETIS lab (ENSEA, France). A web version of the software is available (beta version)!

  • one click to select positive examples,
  • two clicks to select negative examples,
  • then update the search engine.

RETIN: how to
RETIN: how to
  The search engine will return the images it thinks are most relevant, together with a number of images that might be used to improve the search.
RETIN: how to
RETIN: how to

We believe that combining rich image description with learning techniques may provide good solutions for image retrieval tasks. We focused on user interaction systems because it is a natural way to get examples for learning (what the user is looking for). Our RETIN system adapts machine learning techniques and statistical modeling to CBIR. Relevance feedback is modeled as a binary classification. Kernel functions and kernel-based reduction and classification techniques are used. RETIN is designed to grasp a user's query concept quickly despite time and sample constraints. Active learning framework is proposed to optimize this user interaction. See our publications for detailed explanations.