Evaluating machine learning techniques for automatic image annotations

Authors:
Nasullah Khalid Alham;Maozhen Li;Suhel Hammoud;Hao Qi
Affiliations:
School of Engineering and Design, Brunel University, Uxbridge, Unite Kingdom;School of Engineering and Design, Brunel University, Uxbridge, Unite Kingdom;School of Engineering and Design, Brunel University, Uxbridge, Unite Kingdom;School of Engineering and Design, Brunel University, Uxbridge, Unite Kingdom
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
Year:
2009

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Abstract

The past decade has seen a rapid development in Content Based Image Retrieval (CBIR). CBIR is the retrieval of images based on their low level features such as color, texture, shape etc. To improve the retrieval accuracy, the research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the 'semantic gap' between the visual features and the richness of human semantics. Image annotation techniques have been proposed to facilitate CBIR. This paper evaluates 7 representative machine learning techniques for automatic image annotations using 5000 images. An image annotation prototype is implemented and the evaluation results are presented and analyzed.