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Interactive image selection for user annotation aided by the feature space projection

Grant number: 21/06545-5
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): July 01, 2021
Effective date (End): June 30, 2022
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal researcher:Alexandre Xavier Falcão
Grantee:Gabriel Dourado Seabra
Home Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?, AP.TEM


The development of machine learning models based on convolutional neural networks over the years has contributed to important advances in image classification and segmentation problems. Many of these models, however, still rely on a large number of labeled samples for training, which may require considerable user effort and time. Such cost may be prohibitive in areas such as Biology and Medicine. In this sense, the need for developing weakly supervised learning alternatives becomes evident - i.e., learning methods capable of using a few labeled samples to create effective models. Feature Learning from Image Markers (FLIM) fits into this context, as it only uses information about very few samples, with user-marked regions of interest, to extract features that will ultimately be used for training an image classifier. However, there are still unanswered questions regarding FLIM: How should we select the best samples and regions for drawing markers? How can we use the visual impact of these choices in the architectural design of the FLIM extractor? To answer these questions, an interactive interface will be developed that allows image and marker selection to improve the feature space. Such improvement should be evident in the 2D projection of the feature space. The workflow will be validated in the context of image classification and compared to a popular extractor model based on an autoencoder network. (AU)

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