Broad Attribute Prediction Model With Enhanced Attribute and Feature

For the zero-shot image classification without intersection between training and testing sets, the high-quality representation of image attributes and features plays a key role to improve the classification performance. In order to overcome the limitations related to insufficient attribute and feature expression in zero-shot image classification, we propose a broad attribute prediction model with enhanced attribute and feature (EAF-BAP) based on broad learning and elastic net constraint. Firstly, the EAF-BAP enhances pre-defined attributes by elastic net constraint to obtain hybrid attributes, which effectively improves the finiteness of semantic attributes. Secondly, the enhanced features are constructed by broad learning to increase the discrimination ability of features in different classes. Meanwhile, the broad learning is employed to train multiple attribute classifiers synchronously, which is more efficient compared to traditional support vector machines. Finally, the similarity between predicted attributes and hybrid attributes in testing classes is calculated by Manhattan distance, which is further used to implement image classification. Experiments on both AwA and Shoes datasets show that the proposed EAF-BAP model is capable of improving the accuracy of zero-shot image classification efficiently.