A decentralised approach to scene completion using distributed feature hashgram

Scene completion is automated image reconstruction in a plausible way. Typically, semantically valid images are retrieved by pair-wise comparison and subsequently a completion candidate is selected. The primary challenge in scene completion is the computational cost of pair-wise comparisons which increases geometrically with the increase in the number of images. Another challenge is a large number of incoming completion requests which are to be completed on a centralised server. In this work, we propose a decentralised scene completion system using distributed feature hashgram. The system comprises of two principal components, (i) a deep signature-based decentralised image retrieval component that retrieves semantically valid images by way of signature comparison, and (ii) a fog computing enabled scene completion algorithm which finds optimal patches from the most suitable retrieved image to fill in the missing parts using graph-cut technique. A detailed experimental study on LabelMe dataset is performed to evaluate the quality of the solution. Another challenge in scene completion is the absence of ground truth. We propose an evaluation method to evaluate the image completion in the absence of ground truth. The results demonstrate the novelty of the system and the applicability of the solution for large image data repositories.