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.
|Title of host publication||Proceedings of the 2011 6th IEEE Conference on Industrial Electronics and Applications (ICIEA)|
|Place of Publication||Beijing, China|
|Publisher||IEEE, Institute of Electrical and Electronics Engineers|
|Publication status||Published - 2011|
|Event||6th IEEE Conference on Industrial Electronics and Applications (ICIEA2011) - Beijing, China|
Duration: 21 Jun 2011 → 23 Jun 2011
|Conference||6th IEEE Conference on Industrial Electronics and Applications (ICIEA2011)|
|Period||21/06/11 → 23/06/11|
Lei, Y. J., Wong, W., Bennamoun, M., & Liu, W. (2011). Integrating Visual Classifier Ensemble with Term Extraction for Automatic Image Annotation. In Proceedings of the 2011 6th IEEE Conference on Industrial Electronics and Applications (ICIEA) (Vol. 1, pp. 1959-1965). IEEE, Institute of Electrical and Electronics Engineers.