Exploiting Multi-layer Features Using a CNN-RNN Approach for RGB-D Object Recognition [chapter]

Ali Caglayan, Ahmet Burak Can
2019 Lecture Notes in Computer Science  
This paper proposes an approach for RGB-D object recognition by integrating a CNN model with recursive neural networks. It first employs a pre-trained CNN model as the underlying feature extractor to get visual features at different layers for RGB and depth modalities. Then, a deep recursive model is applied to map these features into highlevel representations. Finally, multi-level information is fused to produce a strong global representation of the entire object image. In order to utilize the
more » ... CNN model trained on large-scale RGB datasets for depth domain, depth images are converted to a representation similar to RGB images. Experimental results on the Washington RGB-D Object dataset show that the proposed approach outperforms previous approaches.
doi:10.1007/978-3-030-11015-4_51 fatcat:a4iezbiv3vfrraal333akeipba