Paper Info
Title
Improving Tagging Quality via Learning Dissimilarity Measure in Non-Euclidean Spaces
Abstract
The overwhelming proliferation of digital images on media sharing webs have triggered the requirement of effective tools to retrieve images of interest using semantic concepts. Due to the semantic gap between low-level visual features and high-level semantic concepts of an image, however, the performances of many existing automatic image annotation algorithms are not so satisfactory. In this paper, a novel image classification scheme, named high order statistics based maximum a posterior (HOS-MAP). This method first utilizes high order statistics to measure the triplet-dissimilarity to better describe the relevance among images, then utilizes a maximum of a posterior algorithm with the information of Gaussian Mixture Model and dissimilarity increments distribution to estimate the relevance scores of each tag. Experimental results on a general-purpose image database demonstrate the effectiveness and efficiency of the proposed scheme.
Year
DOI
Venue
2013
10.1109/ACPR.2013.112
ACPR
Keywords
Field
DocType
high order statistic,high-level semantic concept,non-euclidean spaces,digital image,general-purpose image database,posterior algorithm,learning dissimilarity measure,proposed scheme,existing automatic image annotation,semantic gap,novel image classification scheme,semantic concept
Non-Euclidean geometry,Automatic image annotation,Pattern recognition,Semantic gap,Digital image,Artificial intelligence,Maximum a posteriori estimation,Order statistic,Contextual image classification,Mathematics,Mixture model
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Juanjuan Hu101.01
Songhao Zhu28812.88
Baojie Fan34110.48