Investigating the Gestalt Principle of Closure in Deep Convolutional Neural Networks

2024/11/01 by Yuyan Zhang, Derya Soydaner, Zhang, Yuyan +7
Engineering · Computer Science · #Human Motion and Animation #Virtual Reality Applications and Impacts

paper · pdf · doi:10.48550/arxiv.2411.00627

Abstract

Deep neural networks perform well in object recognition, but do they perceive objects like humans? This study investigates the Gestalt principle of closure in convolutional neural networks. We propose a protocol to identify closure and conduct experiments using simple visual stimuli with progressively removed edge sections. We evaluate well-known networks on their ability to classify incomplete polygons. Our findings reveal a performance degradation as the edge removal percentage increases, indicating that current models heavily rely on complete edge information for accurate classification. The data used in our study is available on Github.

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