Multi-View CNN Feature Aggregation with ELM Auto-Encoder for 3D Shape Recognition
Multi-View CNN Feature Aggregation with ELM Auto-Encoder for 3D Shape Recognition is a scholarly work, published in 2018 in ''Cognitive Computation''. The main subjects of the publication include classifier, convolutional neural network, artificial intelligence, feature extraction, computer vision, encoder, computation, extreme learning machine, feature, pattern recognition, and computer science. Experimental results on the benchmarking Princeton ModelNet, ShapeNet Core 55, and PSB datasets show that the proposed framework achieves higher classification and retrieval accuracy in much shorter time than the state-of-the-art methods.