Discriminative semantic region selection for fine-grained recognition

Abstract

Performances of fine-grained recognition have been greatly improved thanks to the fast developments of deep convolutional neural networks (DCNN). DCNN methods often treat each image region equally. Besides, researchers often rely on visual information for classification. To solve these problems, we propose a novel discriminative semantic region selection method for fine-grained recognition (DSRS). We first select a few image regions and then use the pre-trained DCNN models to predict their semantic correlations with corresponding classes. We use both visual and semantic representations to represent image regions. The visual and semantic representations are then linearly combined for joint representation. The combination parameters are determined by considering both semantic distinctiveness and spatial-semantic correlations. We use the joint representations for classifier training. A testing image can be classified by obtaining the visual and semantic representations and encoded for joint representation and classification. Experiments on several publicly available datasets demonstrate the proposed method's superiority.