CLIPS-LIS-LSR Video search system (Grenoble, France)

CLIPS-LIS-LSR Video search system (Grenoble, France)

Georges Quénot, Laurent Besacier, Mbarek Charhad

Stephane Ayache, Daniel Moraru, Philippe Mulhem

CLIPS-IMAG

Denis Pellerin, Mickael Guironnet,

LIS/SIC

Jérôme Gensel,

LSR/SIGMA

Contact : Georges.Quenot@imag.fr

CLIPS-LIS-LSR search system (Grenoble, France)http://korsakov1.imag.fr/cvsp/trecvid.html

• 5 criteria:– Keywords from ASR– Visual similarity to query image (color & texture)– 15 semantic categories (P. Mulhem)– Visual similarity to positives from feedback– Temporal closeness to positives from feedback

• User-weighted linear combination• Very fast and efficient feedback (optimized user

interface and search server)

CLIPS-LIS-LSR search system (Grenoble, France)Starting screen

CLIPS-LIS-LSR search system (Grenoble, France)Topic presentation and first query

CLIPS-LIS-LSR search system (Grenoble, France)Topic presentation and first query

Query Input

Enlarged View

Topic Presentation

Topic Text

Image Examples

Video Examples

CLIPS-LIS-LSR search system (Grenoble, France)Query reformulation and relevance feedback

CLIPS-LIS-LSR search system (Grenoble, France)Query reformulation and relevance feedback

Query Reformu-lation

Enlarged View Relevance Feedback

CLIPS-LIS-LSR search system (Grenoble, France)Query Input / Reformulation

Select and weight query modes

Enter Keywords

Enter Example Image References

CLIPS-LIS-LSR search system (Grenoble, France)Query Input / Reformulation

Enter searched semantic features:

Left: unwanted Central: neutral Right: desired

CLIPS-LIS-LSR search system (Grenoble, France)Relevance Feedback

Search Similars

Mark Positive

Mark Unsure

Mark Negative

Play shot

Search Neighbors

CLIPS-LIS-LSR search system (Grenoble, France)Evolution of MAP over time

CLIPS-LIS-LSR search system (Grenoble, France)System / User performance

• There is a significant variability of the system performance according to the user.

• The relative user performance is consistent with the knowledge and the experience the user has of the system.

• It is also most probable that the mother language as well as the cultural background of the users significantly affect the system/user performance.

• None of the users here is an English native speaker.

• None of them either is much familiar with the politics and sports in the US.