Bermejo, P., Joho, H., Jose, J. and Villa, R. (2009) Comparison of feature construction methods for video relevance prediction. Lecture Notes in Computer Science, 5371, pp. 185-196. (doi: 10.1007/978-3-540-92892-8_19)
Full text not currently available from Enlighten.
Publisher's URL: http://dx.doi.org/10.1007/978-3-540-92892-8_19
Abstract
Low level features of multimedia content often have limited power to discriminate a document’s relevance to a query. This motivated researchers to investigate other types of features. In this paper, we investigated four groups of features: low-level object features, behavioural features, vocabulary features, and window-based vocabulary features, to predict the relevance of shots in video retrieval. Search logs from two user studies formed the basis of our evaluation. The experimental results show that the window-based vocabulary features performed best. The behavioural features also showed a promising result, which is useful when the vocabulary features are not available. We also discuss the performance of classifiers.
Item Type: | Articles |
---|---|
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Jose, Professor Joemon and Joho, Mr Hideo and Villa, Dr Robert |
Authors: | Bermejo, P., Joho, H., Jose, J., and Villa, R. |
Subjects: | Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
College/School: | College of Science and Engineering > School of Computing Science |
Journal Name: | Lecture Notes in Computer Science |
Publisher: | Springer |
ISSN: | 0302-9743 |
ISSN (Online): | 1611-3349 |
University Staff: Request a correction | Enlighten Editors: Update this record