TREC Feature Extraction by Active Learning
J. Vendrig, Jurgen den Hartog, Dirk J. van Leeuwen, Ioannis Patras, Stephan Raaijmakers, Cees G. M. Snoek, J.C.J. Rest, Marcel Worring · UvA-DARE (University of Amsterdam) · 2002
this paper the user's input is given at index-time, rather than at query-time as done in our TREC 2001 contribution [2]. In [2], we associated user queries with video content descriptors via general Wordnet concepts. For example, query term woman maps to Wordnet hypernyms "person, individual, human" which we associated with the "face presence" descriptor. In this TREC 2002 contribution, we focus on building models for the association of content descriptors with generic concepts, such as the Wordnet hypernyms. Specifically, we focus on the ten generic concepts given by the TREC feature extraction task . User and machine interact in order to map the semantic feature concept to content descriptors for a training set, so that shots can be classified for use in retrieval applications