Learning Connectedness in Binary Images
نویسندگان
چکیده
This paper proposes a new Eye-based Recurrent Network Architecture (ERNA) for image classification. The new architecture is trained by a combination of Qlearning and RPROP. The classification performance is compared with other network architectures on the task of determining connectedness between pixels in small binary images. The experiments show that ERNA outperforms both the standard multi-layer perceptron network and the fully-connected recurrent network on the task mentioned above. This performance leads us to the conclusion that the eye facilitates learning in the topologically-structured domain of image classification.
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