Utilization of rotation-invariant uniform LBP histogram distribution and statistics of connected regions in automatic image annotation based on multi-label learning

Abstract

A method for automatic image annotation based on multi-feature fusion and multi-label learning algorithm was proposed in this paper. In the process of feature fusion, rotation-invariant uniform local binary pattern histogram distribution and counting of connected regions in image were extracted and utilized fully. Besides traditional n-order color moments and texture information, rotation-invariant uniform LBP histogram distribution, connected regions number, weighted histogram's integral were appended to image features which aided to improve the average precision. Based on multi-label learning k-nearest neighbor algorithm and Corel5k image data set, comparisons among different dimensional features combinations were made to show that the proposed method outperformed that of traditional one with only basic color moments and texture distribution. The average precision was showed to be improved from 0.2898 to 0.3954 in automatic image annotation in our experimental results.