Feature selection under learning to rank model for multimedia retrieve

Li, Changsheng, Shao, Ling, Xu, Changsheng and Lu, Hanqing (2010) Feature selection under learning to rank model for multimedia retrieve. In: ICIMCS '10 - 2nd International Conference on Internet Multimedia Computing and Service, 30th - 31st December 2010, Harbin, China.

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Abstract

Most multimedia retrieval problem can be described by ranking model, i.e. the images in the database could be ranked according to the similarity compared with the query image. Existing ranking models generally use the features that are pre-defined by experts. This paper utilized machine learning techniques to automatically select useful features for ranking. We first generate a set of feature subsets by putting each feature into an individual feature subset. Then we sort these feature subsets according to the ranking performances. Third, two neighbor feature subsets in the ranked order are pairwised to generate a new feature subset. The new feature subsets are sorted based on the new ranking performance. Iterate until reach the pre-defined stop point. Experimental results on .gov dataset and Caltech101 development set show the effectiveness and efficiency of the proposed algorithm.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Feature selection, learning to rank, evaluation measure, multimedia retrieval
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Paul Burns
Date Deposited:	17 Jun 2015 10:09
Last Modified:	13 Oct 2019 00:31
URI:	http://nrl.northumbria.ac.uk/id/eprint/22979

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