Image labeling by multiple segmentation

In this paper, we provide a method for image labeling by combining the local features and contextual cues in a multiple segmentation framework. Our main insight is to weight the classification results of each image region in different levels, which are obtained by a series of learned discriminative models based on bag of features. The contextual cues are implicitly embedded as feature selection in learning process. Multiple segmentation framework provides robust representation, allowing a wide variety of cues to contribute to the confidence in each semantic label. Our algorithm has been applied on the lotus hill institute(LHI) 15-class dataset and outperforms other state-of-the-art methods.