Learning object classes from image thumbnails through deep neural networks
We propose a new approach for recognizing object classes which is based on the intuitive idea that human beings are able to perform the task well given only thumbnails (coarse scale version) of images. Unlike previous work which uses local image features at fine scales, our approach uses thumbnails directly, and captures their high-order correlations at coarse scales through deep multi-layer neural networks based on restricted Boltzmann machines. Specifically, the pretraining stage of such networks takes on the role of feature extraction. Experimental results show that the proposed approach is comparable to other state-of-the-art recognition methods in terms of accuracy. The merits of the proposed approach come from the simplicity of the workflow and the parallelizability of the implementation structure.