Scenique: a multimodal image retrieval interface

ABSTRACT

Searching for images by using low-level visual features, such as color and texture, is known to be a powerful, yet imprecise, retrieval paradigm. The same is true if search relies only on keywords (or tags), either derived from the image context or user-provided annotations. In this demo we present Scenique, a multimodal image retrieval system that provides the user with two basic facilities: 1) an image annotator, that is able to predict keywords for new (i.e., unlabelled) images, and 2) an integrated query facility that allows the user to search for images using both visual features and tags, possibly organized in semantic dimensions. We demonstrate the accuracy of image annotation and the improved precision that Scenique obtains with respect to querying with either only features or keywords.

REFERENCES

• 1: I. Bartolini and P. Ciaccia. Imagination: Accurate Image Annotation Using Link-analysis Techniques. In AMR 2007, Paris, France, July 2007.
• 2: Ronald Fagin , R. Guha , Ravi Kumar , Jasmine Novak , D. Sivakumar , Andrew Tomkins, Multi-structural databases, Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, June 13-15, 2005, Baltimore, Maryland [doi>10.1145/1065167.1065191]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Image databases
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Abstracting methods

General Terms:
Design, Performance

Keywords:
multi-structural databases, semantic dimensions, visual features