Date of Graduation
Bachelor of Science
Computer Science and Computer Engineering
Committee Member/Second Reader
In this paper, we compare the results of ResNet image classification with the results of Google Image search. We created a collection of 1,000 images by performing ten Google Image searches with a variety of search terms. We classified each of these images using ResNet and inspected the results. The ResNet classifier predicted the category that matched the search term of the image 77.5% of the time. In our best case, with the search term “forklift”, the classifier categorized 92 of the 100 images as forklifts. In the worst case, for the category “hammer”, the classifier matched the search term 61 times out of 100. We also leveraged the prediction confidence levels of the ResNet classifier to determine the relative similarity of an image within a set of images. In typical usage of an image classifier, only the most confident prediction is utilized. By using a larger piece of the output vector of the ResNet classifier, we were able to calculate distances between images in feature space. We created visualizations of the distance between images in sets of 100 images.
computer vision, convolutional neural network, neural network, AI, image classification
Smith, D. (2018). Comparison of Google Image Search and ResNet Image Classification Using Image Similarity Metrics. Computer Science and Computer Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/csceuht/56