A PMJ-inspired cognitive framework for natural scene categorization in line drawings
A PMJ-inspired cognitive framework for natural scene categorization in line drawings
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DOI：
10.1016/j.neucom.2015.09.046
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发表时间：
2016-01
期刊：
影响因子：
6
通讯作者：
Minjing Yu;Yong-Jin Liu;Sujing Wang;Qiufang Fu;Xiaolan Fu
中科院分区：
文献类型：
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作者：
Minjing Yu;Yong-Jin Liu;Sujing Wang;Qiufang Fu;Xiaolan Fu
人类׳对自然场景的快速分类能力在神经科学中得到了广泛的研究。最近，一项功能磁共振成像(FMRI)研究表明，在人脑中，从线条画中解码自然场景与从彩色照片中解码非常相似。在最近提出的感知、记忆和判断的计算认知模型(PMJ模型)的基础上，我们研究了线条画的计算模型，并提出了一种基于PMJ模型的线条画自然场景分类的认知框架。使用了俄亥俄州立大学(OSU)的数据集，其中包括海滩、城市街道、森林、高速公路、山脉和办公室六个类别的475张彩色照片，以及由训练有素的艺术家制作的475幅相应的线条画。实验结果表明，我们提出的认知框架在留一交叉验证中获得了48.4%的识别率，远远高于fMRI数据驱动的视觉处理层次的解码准确率(V1的识别率为29%，V2+VP的识别率为27%，V4的识别率为26%，PPA的识别率为29%，RSC的识别率为23%)。
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).