Evaluation of key frame-based retrieval techniques for video
is a Article , Academic article

Outgoing links

Property Object
Dataset Open Research Online
Is part of
Date 2003
Status Peer reviewed
Volume 92
Issue 2-3
Abstract We investigate the application of a variety of content-based image retrieval techniques to the problem of video retrieval. We generate large numbers of features for each of the key frames selected by a highly effective shot boundary detection algorithm to facilitate a query by example type search. The retrieval performance of two learning methods, boosting and k-nearest neighbours, is compared against a vector space model. We carry out a novel and extensive evaluation to demonstrate and compare the usefulness of these algorithms for video retrieval tasks using a carefully created test collection of over 6000 still images, where performance is measured against relevance judgements based on human image annotations. Three types of experiment are carried out: classification tasks, category searches (both related to automated annotation and summarisation of video material) and real world searches (for navigation and entry point finding). We also show graphical results of real video search tasks using the algorithms, which have not previously been applied to video material in this way.
Authors authors
Label Pickering, Marcus and Rüger, Stefan (2003). Evaluation of key frame-based retrieval techniques for video. Computer Vision and Image Understanding, 92(2-3) pp. 217–235.
Title Evaluation of key frame-based retrieval techniques for video