Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/19975
Title: Semi-supervised manifold ordinal regression for image ranking
Authors: Liu, Y
Liu, Y 
Zhong, S
Chan, KCC 
Keywords: Image ranking
Manifold learning
Manifold ordinal regression
Ordinal regression
Semi-supervised learning
Semi-supervised manifold ordinal regression
Issue Date: 2011
Source: Mm'11 - proceedings of the 2011 acm multimedia conference and co-located workshops, 2011, p. 1393-1396 How to cite?
Abstract: In this paper, we present a novel algorithm called manifold ordinal regression (MOR) for image ranking. By modeling the manifold information in the objective function, MOR is capable of uncovering the intrinsically nonlinear structure held by the image data sets. By optimizing the ranking information of the training data sets, the proposed algorithm provides faithful rating to the new coming images. To offer more general solution for the real-word tasks, we further provide the semi-supervised manifold ordinal regression (SS-MOR). Experiments on various data sets validate the effectiveness of the proposed algorithms.
Description: 19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11, Scottsdale, AZ, 28-1 December 2011
URI: http://hdl.handle.net/10397/19975
ISBN: 9781450306164
DOI: 10.1145/2072298.2072023
Appears in Collections:Conference Paper

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