Abstract:Material image recognition has broad application prospects, such as clothing recognition, automatic picking by a robot, industrial detection, etc. Owing to the influence of light intensity and camera angle, material image is easy to change. Mining robust and effective image features is one of the most important factors to handle this change. To address this problem, a novel model called SECF2 is proposed. Heterogeneous-layer features in the SENet model are extracted respectively. These heterogeneous-layer features can complement each other well. Then the traditional Cluster-CCA model is modified to complete the Feature Fusion of these heterogeneous-layer features. As a significant result, more discriminant and robust deep-level visual semantics is obtained after feature fusion to better characterize the material image. Finally, the deep-level visual semantics is employed to train classification models, and a voting-based ensemble learning strategy is designed to further boost the final classification performance. Experimental results demonstrate that the SECF2 model is effective on two material image datasets. Especially, compared with the best baseline, it obtains a 8.85% performance improvement in the Fabric dataset. In addition, SECF2 model has strong versatility. In the image sentiment analysis task, it has achieved excellent performance. Moreover, the proposed SECF2 model only needs two features and one time feature fusion. Hence, the model obtains better real-time efficiency.