Take a look at the new version of this page: [ beta version ]. Tell us what you think.

ABSTRACT

We introduce an interface for efficient video search that exploits the human ability to quickly scan visual content, after an automatic system has done its best to arrange the images in order of relevance. While extreme video retrieval is taxing to the human, it has also been shown to be very effective. The system will demonstrate several ways to rapidly scan images, change search queries, and employ different types of relevance feedback.

REFERENCES

• 1: Alexander G. Hauptmann , Wei-Hao Lin , Rong Yan , Jun Yang , Ming-Yu Chen, Extreme video retrieval: joint maximization of human and computer performance, Proceedings of the 14th annual ACM international conference on Multimedia, October 23-27, 2006, Santa Barbara, CA, USA [doi>10.1145/1180639.1180721]
• 2: Jun Yang , Yu-Gang Jiang , Alexander G. Hauptmann , Chong-Wah Ngo, Evaluating bag-of-visual-words representations in scene classification, Proceedings of the international workshop on Workshop on multimedia information retrieval, September 24-29, 2007, Augsburg, Bavaria, Germany [doi>10.1145/1290082.1290111]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.1 Multimedia Information Systems
          Subjects: Evaluation/methodology

Additional Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.1 Multimedia Information Systems
          Subjects: Video (e.g., tape, disk, DVI)

General Terms:
Experimentation, Human Factors

Keywords:
multi-modality integration, relevance feedback, video retrieval