Labeling images by integrating sparse multiple distance learning and semantic context modeling

Abstract

Recent progress on Automatic Image Annotation (AIA) is achieved by either exploiting low level visual features or high level semantic context. Integrating these two paradigms to further leverage the performance of AIA is promising. However, very few previous works have studied this issue in a unified framework. In this paper, we propose a unified model based on Conditional Random Fields (CRF), which establishes tight interaction between visual features and semantic context. In particular, Kernelized Logistic Regression (KLR) with multiple visual distance learning is embedded into the CRF framework. We introduce L 1 and L 2 regularization terms into the unified learning process for the distance learning and the parameters penalty respectively. The experiments are conducted on two benchmarks: Corel and TRECVID-2005 data sets for evaluation. The experimental results show that, compared with the state-of-the-art methods, the unified model achieves significant improvement on annotation performance and shows more robustness with increasing number of various visual features. © 2012 Springer-Verlag.