Audiovisual cross-modal material surface retrieval

Cross-modal retrieval is developed rapidly because it can process the data among different modalities. Aiming at solving the problem that the text and image sometimes cannot perform the true and accurate analysis of the material, a system of audiovisual cross-modal retrieval on material surface is proposed. First, we use local receptive fields-based extreme learning machine to extract sound and image features, and then the sound and image features are mapped to the subspace using canonical correlation analysis and retrieved by Euclidean distance. Finally, the process of audiovisual cross-modal retrieval is realized by the system. The experimental results show that the proposed system has a good application effect on wood. The designed system provides a new idea for research in the field of material identification.