With great developments of computing technologies and data mining methods, image annotation has attracted much attraction in\nsmart agriculture. However, the semantic gap between labels and images poses great challenges on image annotation in agriculture,\ndue to the label imbalance and difficulties in understanding obscure relationships of images and labels. In this paper, an image\nannotation method based on graph learning is proposed to accurately annotate images. Specifically, inspired by nearest\nneighbors, the semantic neighbor graph is introduced to generate preannotation, balancing unbalanced labels. Then, the\ncorrelations between labels and images are modeled by the random dot product graph, to deeply mine semantics. Finally, we\nperform experiments on two image sets. The experimental results show that our method is much better than the previous\nmethod, which verifies the effectiveness of our model and the proposed method.
Loading....