Gaussian Process Density Counting from Weak Supervision

As a novel learning setup, we introduce learning to count objects within an image from only region-level count information. This level of supervision is weaker than earlier approaches that require segmenting, drawing bounding boxes, or putting dots on centroids of all objects within training images. We devise a weakly supervised kernel learner that achieves higher count accuracies than previous counting models. We achieve this by placing a Gaussian process prior on a latent function the square of which is the count density. We impose non-negativeness and smooth the GP response as an intermediary step in model inference. We illustrate the effectiveness of our model on two benchmark applications: i) synthetic cell and ii) pedestrian counting, and one novel application: iii) erythrocyte counting on blood samples of malaria patients.