Image annotation based on positive-negative instances learning

Kai Zhang; Jiwei Hu; Quan Liu; Ping Lou

doi:10.1117/12.2281765

21 July 2017 Image annotation based on positive-negative instances learning

Kai Zhang, Jiwei Hu, Quan Liu, Ping Lou

Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104201T (2017) https://doi.org/10.1117/12.2281765
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China

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.

Citation Download Citation

Kai Zhang, Jiwei Hu, Quan Liu, and Ping Lou "Image annotation based on positive-negative instances learning", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104201T (21 July 2017); https://doi.org/10.1117/12.2281765

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