NDLI: A novel approach for improving image annotation quality

Content Provider	IEEE Xplore Digital Library
Author	Zhu Sonhao Liu Jiawei Hu Ronglin
Copyright Year	2014
Description	Author affiliation: Yangzhou Suan IOT Sensor Technol. Co., Ltd., Yangzhou, China (Hu Ronglin) \|\| Sch. of Autom., Nanjing Univ. of Post & Telecommun., Nanjing, China (Zhu Sonhao; Liu Jiawei)
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. 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.
Starting Page	3720
Ending Page	3724
File Size	638198
Page Count	5
File Format	PDF
ISBN	9781479937073
e-ISBN	9781479937080
DOI	10.1109/CCDC.2014.6852826
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-31
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	High-Order Statistics Maximum A Posteriori Computer vision Visualization Gaussian Mixture Model Conferences Semantics Dissimilarity Increments Distribution Electronic mail Classification algorithms Multimedia communication Non-Euclidean Space
Content Type	Text
Resource Type	Article

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