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Homology of human and machine vision systems demonstrates that better machine could be designed with human assistance. Similar components can be mapped from neuroimaging data to visual features for recognizing an object. However, inferring object relationships from human vision and machine vision are not clear. To measure the similarity of human and machine visual inference, this work study an inference method using Microsoft COCO dataset. The input data is manually generated, and used for a java-based inference engine, which collects semantic data in a co-occurrence matrix, and writes the data to a knowledge graph in the DOT language. Unlike the black-box property of deep neural network, the proposed method is transparent. When rendered by GraphViz tools, the visible results in the knowledge graph indicated that the COCO dataset-based machine inference is promising when compared to human inference, yielding an accuracy of 64% at best. This novel inference study on the COCO dataset reveals that homology of human and machine vision systems is promising to be bridged. Bigger dataset and more concepts may increase the accuracy in the future work.