A PMJ-Inspired cognitive framework for natural scene categorization in line drawings
Published in Neurocomputing, 2016
Abstract: Humans׳ remarkable capacity on rapid natural scene categorization has been widely studied in neuroscience. Recently, a functional MRI (fMRI) study showed that in human brain, decoding of natural scenes from line drawings was very similar to those from color photographs. In this paper, based on recently proposed computational cognition model of Perception, Memory and Judgement (PMJ model), we investigate the computational model of line drawings and propose a PMJ-inspired cognitive framework for natural scene categorization in line drawings. The Ohio State University (OSU) dataset was used, which included 475 color photographs in six categories, i.e., beaches, city streets, forests, highways, mountains and offices, as well as 475 corresponding line drawings produced by trained artists. Experimental results show that our proposed cognitive framework achieves 48.4% recognition rate in leave-one-out cross-validation, which is much higher than fMRI-data-driven decoding accuracy in the visual-processing hierarchy (29% in V1, 27% in V2+VP, 26% in V4, 29% in PPA and 23% in RSC).
Recommended citation: Minjing Yu, Yong-Jin Liu*, Su-Jing Wang, Qiufang Fu, Xiaolan Fu (2016) A PMJ-Inspired cognitive framework for natural scene categorization in line drawings. Neurocomputing, Vol. 173, Part 3, pp. 2041–2048, 2016.