Investigating the Gestalt Principle of Closure in Deep Convolutional Neural Networks

• URL: http://arxiv.org/abs/2411.00627v1
• Date: Fri, 01 Nov 2024 14:36:21 GMT
• Title: Investigating the Gestalt Principle of Closure in Deep Convolutional Neural Networks
• Authors: Yuyan Zhang, Derya Soydaner, Fatemeh Behrad, Lisa Koßmann, Johan Wagemans,
• Abstract summary: This study investigates the principle of closure in convolutional neural networks. We conduct experiments using simple visual stimuli with progressively removed edge sections. We evaluate well-known networks on their ability to classify incomplete polygons.
• Score: 4.406699323036466
• License:
• 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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