We present a spatial and temporal analysis of Liquid Crystal sensor responses to different chemical stimuli for classification. This work focuses upon the development of highly accurate classification while minimizing the number of required features. Previous work conducted by Cao and co-workers developed an accurate linear classifier with features extracted from Alexnet and basic image analysis techniques such as Histogram of Oriented Gradients. We extend this work by analyzing the effectiveness of features extracted from the VGG16 network, which is more compact than Alexnet, and utilizes smaller convolutional filters. Our findings demonstrate that features extracted from the first and second convolutional block of VGG16 allow for perfect linear classification on the given dataset while reducing the number of required features one hundred (100) fold. The number of features is further reduced to ten (10) via recursive feature elimination with minimal losses in classification accuracy. Analysis of the reduced feature set provides a window into the physical reasoning behind the accurate linear classification.

Space-time analysis of optical responses of liquid crystal sensors

2019 Spring National Meeting

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