Learning robust independent bases for accurate scene categorization
Zhao Xie, Ran Ling, Kewei Wu, Jun Gao · 2012
Due to the importance of feature extraction and scene representation in classification tasks, this paper presents an approach for unsupervised feature learning using Independent Subspace Analysis. The optimization process of feature bases is incorporated into the framework of incremental learning to cope with the learning difficulty with large or dynamic samples. The proposed method could automatically learn image features and accomplish scene classification with Spatial Pyramid Matching model. Also, the influence of related parameters in optimization and classification is discussed. Experiment shows the proposed method constructs efficient scene description and outperforms several previous methods in classification on OT scene dataset.