Integrating visual classifier ensemble with term extraction for Automatic Image Annotation
Yinjie Lei, Wilson Wong, Mohammed Bennamoun, Wei Liu · 2011
Existing Automatic Image Annotation (AIA) systems are typically developed, trained and tested using high quality, manually labelled images. The tremendous manual efforts required with an untested ability to scale and tolerate noise all have an impact on existing systems' applicability to real-world data. In this paper, we propose a novel AIA system which harnesses the collective intelligence on the Web to automatically construct training data to work with an ensemble of Support Vector Machine (SVM) classifiers based on Multi-Instance Learning (MIL) and global features. An evaluation of the proposed annotation approach using an automatically constructed training set from Wikipedia demonstrates a slight improvement of in annotation accuracy in comparison with two existing systems.