An Active Learning Strategy for SVM-Based Captioning

Remote sensing (RS) image captioning (IC) is a novel technique introduced recently in the RS community to enrich the description of very high resolution (VHR) images. The goal of RSIC is to generate a sentence that summarizes the content of an image. In general, RSIC is developed in a supervised way where annotated samples are needed to train the system. However, obtaining annotated samples is costly and time consuming in particular when the labels are sentence descriptions that are very subjective. In order to cope with the problem of having large amounts of training samples in this work we propose an active learning solution for RSIC that selects the most important samples to label and include in the training. Experimental results performed on UCM caption dataset show the promising effectiveness of the proposed active learning strategy.