Towards evaluating FLIM attention regions - Cerqueira, Matheus A.; Benato, Bárbara C.; Telea, Alexandru C.; Falcão, Alexandre X. (Anais Estendidos da Conference on Graphics, Patterns and Images (SIBGRAPI); 2025: Anais Estendidos da XXXVIII Conference on Graphics, Patterns and Images, 2025)
FLIM (Feature Learning from Image Markers) has been used to interactively train convolutional networks from user-defined attention regions. Although FLIM has shown competitive results with large deep models, resulting in shallow networks trained with very few weakly annotated images, the performance of FLIM models is related to those attention regions. Much effort has been made to improve the FLIM framework itself, but no investigation has been conducted into these marked locations and their relationship to model performance. In this work, we propose to open ways to evaluate the image markers’ location impacts on FLIM. For that, we exploit multiple marker positions, determine their relevance, and create a color code for each location, resulting in a heatmap of marker locations that reflects the FLIM performance from those image regions. From our results, we showed how the FLIM performance differs depending on the background marker location, which provides some trends for better/worse marker scenarios.