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Proceedings Paper

Image annotation based on positive-negative instances learning
Author(s): Kai Zhang; Jiwei Hu; Quan Liu; Ping Lou
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Paper Abstract

Automatic image annotation is now a tough task in computer vision, the main sense of this tech is to deal with managing the massive image on the Internet and assisting intelligent retrieval. This paper designs a new image annotation model based on visual bag of words, using the low level features like color and texture information as well as mid-level feature as SIFT, and mixture the pic2pic, label2pic and label2label correlation to measure the correlation degree of labels and images. We aim to prune the specific features for each single label and formalize the annotation task as a learning process base on Positive-Negative Instances Learning. Experiments are performed using the Corel5K Dataset, and provide a quite promising result when comparing with other existing methods.

Paper Details

Date Published: 21 July 2017
PDF: 6 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104201T (21 July 2017); doi: 10.1117/12.2281765
Show Author Affiliations
Kai Zhang, Wuhan Univ. of Technology (China)
Jiwei Hu, Wuhan Univ. of Technology (China)
Quan Liu, Wuhan Univ. of Technology (China)
Ping Lou, Wuhan Univ. of Technology (China)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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