Closed-Loop Deep Vision

There has been a resurgence of interest in one of the most fundamental aspects of computer vision, which is related to the existence of a feedback mechanism in the inference of a visual classification process. Indeed, this mechanism was present in the first computer vision methodologies, but technical and theoretical issues imposed major roadblocks that forced researchers to seek alternative approaches based on pure feed-forward inference. These open loop approaches process the input image sequentially with increasingly more complex analysis steps, and any mistake made by intermediate steps impair all subsequent analysis tasks. On the other hand, closed-loop approaches involving feed- forward and feedback mechanisms can fix mistakes made during such intermediate stages. In this paper, we present a new closed- loop inference for computer vision problems based on an iterative analysis using deep belief networks (DBN). Specifically, an image is processed using a feed-forward mechanism that will produce a classification result, which is then used to sample an image from the current belief state of the DBN. Then the difference between the input image and the sampled image is fed back to the DBN for re- classification, and this process iterates until convergence. We show that our closed-loop vision inference improves the classification results compared to pure feed-forward mechanisms on the MNIST handwritten digit dataset and the Multiple Object Categories containing shapes of horses, dragonflies, llamas and rhinos.